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Misra, Dheeraj, Sushma Vishnani, and Ankit Mehrotra. "Four-moment CAPM Model: Evidence from the Indian Stock Market." Journal of Emerging Market Finance 18, no. 1_suppl (April 2019): S137—S166. http://dx.doi.org/10.1177/0972652719831564.
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This study aims at analysing the impact of co-skewness and co-kurtosis on the returns of the Indian stocks by incorporating co-skewness and co-kurtosis in the traditional capital asset pricing model (CAPM) of Sharpe, in a three-factor model of Fama and French and in a four-factor model of Carhart. The results of the study show that co-skewness and co-kurtosis have significant impact on the returns of the Indian stock. However, the impact of co-skewness is higher than co-kurtosis. JEL Classification: G11, G12
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Oliveira, Alexandre Silva de, Luis Felipe Dias Lopes, and Eduardo Botti Abbade. "Coassimetria e cocurtose na análise dos preços das ações no mercado financeiro nacional." Revista de Administração da UFSM 3, no. 3 (January 27, 2011): 326–45. http://dx.doi.org/10.5902/198346592502.
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The central issue of this research is to investigate and measuring the stock price in the brazilian financial market. It was investigate the influence of the third and fourth time in the pricing of assets, the influence of co-skewness in correlation with the proxy IBOV stocks, the influence of co-kurtosis in correlation with the proxy IBOV stocks, the influence of co-skewness and co-kurtosis in the correlation between the proxy IBOV and stocks, its performance compared with the CAPM and the increase of the accuracy. Is was developed literature research and study of time series of the stocks that constitutes the Ibovespa index on 30 May 2008, analyzed with the use of multiple regressions with the variables to systematic volatility, the systematic skewness and systematic kurtosis. As a result it was observed conclusively that co-skewness and co-kurtosis do not improve the performance of the model of pricing of assets.
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Hasan, Md Zobaer, and Anton Abdulbasah Kamil. "Contribution of Co-Skewness and Co-Kurtosis of the Higher Moment CAPM for Finding the Technical Efficiency." Economics Research International 2014 (January 16, 2014): 1–9. http://dx.doi.org/10.1155/2014/253527.
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The objective of this paper is to present the technical efficiency of individual companies and their respective groups of Bangladesh stock market (i.e., Dhaka Stock Exchange, DSE) by using two risk factors (co-skewness and co-kurtosis) as the additional input variables in the Stochastic Frontier Analysis (SFA). The co-skewness and co-kurtosis are derived from the Higher Moment Capital Asset Pricing Model (H-CAPM). To investigate the contribution of these two factors, two types of technical efficiency are derived: (1) technical efficiency with considering co-skewness and co-kurtosis (WSK) and (2) technical efficiency without considering co-skewness and co-kurtosis (WOSK). By comparing these two types of technical efficiency, it is noticed that the technical efficiency of WSK is higher than the technical efficiency of WOSK for the individual companies and their respective groups. As per available literature in the context Bangladesh stock market, no study has been conducted thus far to measure technical efficiency of companies and their respective groups by using the risk factors which are derived from the H-CAPM. In this research, the link between H-CAPM and SFA is established for measuring technical efficiency and it is believed that the findings of this study may be applied to other emerging stock markets.
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Liow, Kim Hiang, and Lanz C. W. J. Chan. "Co‐skewness and Co‐kurtosis in Global Real Estate Securities." Journal of Property Research 22, no. 2-3 (June 2005): 163–203. http://dx.doi.org/10.1080/09599910500453798.
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Chaudhary, Rashmi, Dheeraj Misra, and Priti Bakhshi. "Conditional relation between return and co-moments – an empirical study for emerging Indian stock market." Investment Management and Financial Innovations 17, no. 2 (July 2, 2020): 308–19. http://dx.doi.org/10.21511/imfi.17(2).2020.24.
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Due to many theoretical and practical shortcomings of the traditional CAPM model, this study aims at analyzing the CAPM with possible extensions. The analysis aims to know the empirical soundness of Conditional Higher Moment CAPM in emerging India’s capital market. The sample consists of 69 company’s daily stock price data from April 2004 to March 2019 from NSE 100. Panel data analysis is used on 21 cross-sections. The overall results show that when both up and down markets are incorporated separately, all three moments, namely, co-variance, co-skewness, and co-kurtosis, are priced during the normal Indian economy phase. Further, this study states that including higher moments (co-skewness and co-kurtosis) in the two-moment model provides symmetry in both the up and down markets. This is one of the first studies in the Indian Stock market explaining the variation in portfolio returns through panel data analysis by extending CAPM with conditional higher-order co-moments. The portfolio managers should consider skewness and kurtosis along with variance in constructing the optimal portfolios.
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Botond, Benedek, and Nagy Bálint Zsolt. "Co-skewness, co-kurtosis and their implications on asset pricing of cryptocurrencies." International Journal of Financial Markets and Derivatives 8, no. 1 (2021): 65. http://dx.doi.org/10.1504/ijfmd.2021.10036439.
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Zsolt, Nagy Bálint, and Benedek Botond. "Co-skewness, co-kurtosis and their implications on asset pricing of cryptocurrencies." International Journal of Financial Markets and Derivatives 8, no. 1 (2021): 65. http://dx.doi.org/10.1504/ijfmd.2021.113860.
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Arbia, Giuseppe, Riccardo Bramante, and Silvia Facchinetti. "Least Quartic Regression Criterion to Evaluate Systematic Risk in the Presence of Co-Skewness and Co-Kurtosis." Risks 8, no. 3 (September 8, 2020): 95. http://dx.doi.org/10.3390/risks8030095.
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This article proposes a new method for the estimation of the parameters of a simple linear regression model which is based on the minimization of a quartic loss function. The aim is to extend the traditional methodology, based on the normality assumption, to also take into account higher moments and to provide a measure for situations where the phenomenon is characterized by strong non-Gaussian distribution like outliers, multimodality, skewness and kurtosis. Although the proposed method is very general, along with the description of the methodology, we examine its application to finance. In fact, in this field, the contribution of the co-moments in explaining the return-generating process is of paramount importance when evaluating the systematic risk of an asset within the framework of the Capital Asset Pricing Model. We also illustrate a Monte Carlo test of significance on the estimated slope parameter and an application of the method based on the top 300 market capitalization components of the STOXX® Europe 600. A comparison between the slope coefficients evaluated using the ordinary Least Squares (LS) approach and the new Least Quartic (LQ) technique shows that the perception of market risk exposure is best captured by the proposed estimator during market turmoil, and it seems to anticipate the market risk increase typical of these periods. Moreover, by analyzing the out-of-sample risk-adjusted returns we show that the proposed method outperforms the ordinary LS estimator in terms of the most common performance indices. Finally, a bootstrap analysis suggests that significantly different Sharpe ratios between LS and LQ yields and Value at Risk estimates can be considered more accurate in the LQ framework. This study adds insights into market analysis and helps in identifying more precisely potentially risky assets whose extreme behavior is strongly dependent on market behavior.
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Fernandes, Anderson Rocha de J., Simone Evangelista Fonseca, and Robert Aldo Iquiapaza. "Performance measurement models and their influence on net fundraising of investment funds." Revista Contabilidade & Finanças 29, no. 78 (June 18, 2018): 435–51. http://dx.doi.org/10.1590/1808-057x201805330.
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ABSTRACT This article aims to analyze the relation between third- and fourth-order conditions and risk factors and their adequacy to return, performance, and net fundraising. The factors used to determine fund performance and, consequently, their relation with fundraising are: market return, size, book-to-market, profitability, investment, co-skewness, and co-kurtosis. The funds constituting the sample are those classified as Free Stocks (within the period from April 2001 to April 2015). Methodologically, this study has two phases. The first one refers to estimating the parameters that represent fund sensitivity to the factors and the comparison of the capital asset pricing models (CAPM), Fama-French-Carhart 4-factor (FFC), Fama-French 5-factor (FF5), Fama-French 5-factor with momentum (FF5M), added or not with co-moments, by means of the fixed-effects procedure. The second one deals with verifying the relation between performance and net fundraising. The models were reestimated through moving time windows, so that the alpha calculated on each of them represented fund performance within the immediately subsequent period. We also estimated the relation fundraising-performance through cross-section regressions, with rates and age as control variables. The results showed that the co-skewness and co-kurtosis coefficients are not that relevant for determining performance and net fundraising of investment funds. Among the risk factors, market, size, and momentum are the significant parameters for fund returns. The FFC and FF5M models are those with greater explanatory power regarding return specification. There is also evidence of convexity in the relation between performance and fundraising.
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Bouri, Elie, Ladislav Kristoufek, and Nehme Azoury. "Bitcoin and S&P500: Co-movements of high-order moments in the time-frequency domain." PLOS ONE 17, no. 11 (November 22, 2022): e0277924. http://dx.doi.org/10.1371/journal.pone.0277924.
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Interactions between stock and cryptocurrency markets have experienced shifts and changes in their dynamics. In this paper, we study the connection between S&P500 and Bitcoin in higher-order moments, specifically up to the fourth conditional moment, utilizing the time-scale perspective of the wavelet coherence analysis. Using data from 19 August 2011 to 14 January 2022, the results show that the co-movement between Bitcoin and S&P500 is moment-dependent and varies across time and frequency. There is very weak or even non-existent connection between the two markets before 2018. Starting 2018, but mostly 2019 onwards, the interconnections emerge. The co-movements between the volatility of Bitcoin and S&P500 intensified around the COVID-19 outbreak, especially at mid-term scales. For skewness and kurtosis, the co-movement is stronger and more significant at mid- and long-term scales. A partial-wavelet coherence analysis underlines the intermediating role of economic policy uncertainty (EPU) in provoking the Bitcoin-S&P500 nexus. These results reflect the co-movement between US stock and Bitcoin markets beyond the second moment of return distribution and across time scales, suggesting the relevance and importance of considering fat tails and return asymmetry when jointly considering US equity-Bitcoin trading or investments and the policy formulation for the sake of US market stability.
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O'Connor, Philip. "Inferring Risk Preferences Using Synthetic Win Bets in Horse Betting “Exotic” Markets." Journal of Gambling Business and Economics 2, no. 1 (January 2, 2013): 1–29. http://dx.doi.org/10.5750/jgbe.v2i1.522.
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Exotic bets: exactas, trifectas and superfectas are complicated gambles that depend on the ordering of horse in a race that can be studied by converting them into “synthetic” or “virtual” win bets. Using two ways of constructing synthetic win bets, it is shown that the favorite-longshot bias is a poor description of the returns of the trifecta and superfecta synthetic win bet. Rather, consistent with financial markets, the standard deviation of the payout of the synthetic win bet better describes the different returns of synthetic win bets.It is found that the synthetic win market dislikes standard deviation and kurtosis (and other higher-order even moments) and likes skewness (and other higher order odd moments), implying participants conform to standard utility theory in their choice between win and synthetic win bets and are not risk-loving. A co-efficient of relative risk aversion of about 3 is estimated. Including higher-order moments strongly affects the magnitude of utility function estimates.
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E. B., Udah, and Ebi Bassey. "Infrastructure, Human Capital and Industrialization in Nigeria." Nile Journal of Business and Economics 3, no. 6 (August 19, 2017): 58. http://dx.doi.org/10.20321/nilejbe.v3i6.102.
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The objective of this paper had been to shed light on the importance of infrastructure and human capital on industrialization in Nigeria using time series data from 1970 to 2014. The methodology adopted in this paper was first to trace the historical background of the data using such tests as mean, minimum and maximum values, standard deviation, skewness, kurtosis and Jarque-Bera tests. Second, in order to smoothen the data and reduce white noise, the paper adopted Augmented Dickey-Fuller and Phillips-Perron tests for unit root and for co-integration, the paper used Engle-Granger two-step procedure and Johansen method. The paper captured the interrelationship among the variables with Pairwise Granger causality test. Thirdly, the paper proceeded to use Ordinary Least Squares (OLS) estimation technique. The co-integration tests using Engle-Granger two-step and Johansen methods showed that the series are co-integrated, thus, the use of OLS satisfies the Best Linear Unbiased Estimator (BLUE) with minimum variance property. The parsimonious results suggest that gross domestic investment, electricity supply and trade openness are the required elements to accelerate the pace of industrialization in Nigeria. This implied that providing adequate and stable supply of electricity, deepening public and private investments as well as opening the economy to the vagaries of international trade has short and long-termed lasting effect on industrial development. The policy perspective is that government should prioritize the generation and distribution of electricity, increase the quantum of investments in road infrastructure and opening of the economy in order to accelerate the pace of industrialization.
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Chen, Chaoyue, Hongyu Zhuo, Xiawei Wei, and Xuelei Ma. "Contrast-Enhanced MRI Texture Parameters as Potential Prognostic Factors for Primary Central Nervous System Lymphoma Patients Receiving High-Dose Methotrexate-Based Chemotherapy." Contrast Media & Molecular Imaging 2019 (November 12, 2019): 1–7. http://dx.doi.org/10.1155/2019/5481491.
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Introduction. The purpose of this study was to evaluate the prognostic value of texture features on contrast-enhanced magnetic resonance imaging (MRI) for patients with primary central nervous system lymphoma (PCNSL). Methods. In this retrospective study, fifty-two patients diagnosed with PCNSL were enrolled from October 2010 to March 2017. The texture feature of tumor tissue on the histogram-based matrix (histo-) and the grey-level co-occurrence matrix (GLCM) was retrieved by contrast-enhanced T1-weighted imaging before any antitumor treatment. Receiver operating characteristic curve analyses were performed to obtain their optimal cutoff values, based on which we dichotomized patients into subgroups. The Kaplan–Meier analyses were conducted to compare overall survival (OS) of subgroups, and multivariate Cox regression analyses were used to determine if they could be taken as independent prognostic factors. Results. Ten texture features were extracted from the MR image, including Energy, Entropy, Kurtosis, Skewness on the histogram-based matrix, and Correlation, Contrast, Dissimilarity, Energy, Entropy, and Homogeneity on the grey-level co-occurrence matrix. Three of them (GLCM-Contrast, GLCM-Dissimilarity, and GLCM-Homogeneity) are shown to be significant in relation to overall survival (OS). The multivariate Cox regression analyses suggest that GLCM-Homogeneity could be taken as independent predictors. Conclusions. The texture features of contrast-enhanced magnetic resonance imaging (MRI) could potentially serve as prognostic biomarkers for PCNSL patients.
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Karim, Md Abdul, and Jesmin Akter. "Patterns and Differentials of Onset of Menstruation among School Girls in Chittagong Metropolitan Area of Bangladesh." Chittagong University Journal of Science 41, no. 1 (February 8, 2021): 178–99. http://dx.doi.org/10.3329/cujs.v41i1.51922.
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Onset of menstruation is the biological and physical indicator, which is one of the important components of female reproductive characteristics. Considering its importance in the context of reproductive health and fertility, the aim of this study is to determine the mean age at menarche and also to investigate the patterns and differentials of such an important vital event of the randomly selected girls aged 9-15 years from the schools of Chittagong metropolitan area. The results of this study show that the overall mean menarcheal age of the selected girls is only 11.75±0.97 years with significant variations by their background characteristics. The co-efficient of variation (8.3%) indicates that there exists extreme heterogeneity in menarcheal age of the respondents. Co-efficient of skewness (β1=0.15) and excess of kurtosis (γ2=0.35) reflect that the shape characteristics of age at menarche is positively skewed and leptokurtic. The mean age at menarche is computed as relatively low (11.37 years) among the respondents residing in the metropolitan areas. The mean age at menarche is found the highest (12.01 years) among the underweight and the lowest among overweight (11.43 years) girls. The results from the life table technique show that unexpectedly 1.2% girls likely to attain menarche only within age of 9.67 years. The value of spread (s=16 months) shows extreme heterogeneity in menarcheal age. The values of trimean of onset of menstruation for underweight, normal and overweight girls are found 11.83, 11.58 and 11.41 years respectively.
The Chittagong Univ. J. Sci. 40(1) : 178-199, 2019
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Karim, Md Abdul, and Jesmin Akter. "Determinants of Nutritional Status among School Girls in Chittagong Metropolitan Area." Chittagong University Journal of Science 41, no. 1 (February 8, 2021): 39–67. http://dx.doi.org/10.3329/cujs.v41i1.51914.
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This study aims to investigate the determinants of nutritional status (BMI) of school girls, selected randomly from the schools of Chittagong metropolitan area because good nutritional status is a prerequisite for good health, fertility and national productivity. The results of this study show that more than one-third (38.6%) school girls belong to underweight, 47.9% normal and 13.5% overweight. The overall mean BMI of the selected girls is found 20.03±4.06 kg/m2 with considerable variations by their background characteristics. The co-efficient of variation (20.27%) indicates that there exists extreme heterogeneity in BMI of the respondents. Co-efficient of skewness (β1=0.85) and excess of kurtosis (γ2=1.05) reflect that the distribution of BMI is positively skewed and leptokurtic. The mean BMI is found relatively high among the respondents living in the metropolitan area (21.18 kg/m2). The highest mean BMI is found among the girls belong to high family income group (21.62 kg/m2) and low (18.69 kg/m2) in lower family income group. Bivariate analysis indicates that religion, place of origin, place of residence, respondents’ education, arm circumference, fathers and mothers education and occupation, family income, family size, sibling size, skipping and cycling, duration of sporting activity and sleeping, and food intake are found to have significant association with nutritional status of the girls. The study also shows that BMI is significantly positively correlated with family income and negatively with duration of sporting activities. Multinomial logistic regression analysis illustrates that place of residence, arm circumference, mothers’ occupation, duration of sleeping and food intake is found significant predictors of BMI.
The Chittagong Univ. J. Sci. 40(1) : 39-67, 2019
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Didenkulova, E. G., A. V. Slunyaev, and E. N. Pelinovsky. "NUMERICAL SIMULATION OF IRREGULAR BIMODAL WAVE SYSTEM DYNAMICS WITHIN THE FRAMEWORK OF KORTEWEG-DE VRIES EQUATION." XXII workshop of the Council of nonlinear dynamics of the Russian Academy of Sciences 47, no. 1 (April 30, 2019): 38–40. http://dx.doi.org/10.29006/1564-2291.jor-2019.47(1).10.
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The dynamics of wave ensembles in shallow water is studied within the framework of the nonlinear dispersive Korteweg – de Vries (KdV) equation by numerical simulation. Bimodal wave systems whose energy is distributed over two spectral domains are considered: the “additional” lobe which corresponds to the system of longer or shorter waves is added to the “main” spectral peak. The concerned problem describes, for example, the interaction between wind waves and swell in shallow water. The case of the unimodal waves (considered in (Pelinovsky, Sergeeva, 2006) is used as the reference. The limitations of the implied assumptions and the relationship of the idealized model to the realistic conditions in the ocean were discussed in the recent paper (Wang et al, 2018). Based on the detailed consideration of the 6 simulated cases, the following general conclusions may be formulated. The transition from the initial state to the quasi-equilibrium one is accompanied by strong variations of the wave characteristics, when the waves exhibit the most extreme features. In particular, the wave kurtosis grows suddenly and the abnormal heavy tails in the wave amplitude probability distributions appear. These processes are observed in all the cases of the bimodal spectra and are quite similar to the single-mode regime. The coexistence of a long-wave system smoothens the rapid oscillations of the wave extremes and kurtosis which take place during the transition stage. The presence of a short-wave system makes the waves on average more symmetric. Skewness attains the minimum value compared to the other cases. The co-existence of shorter waves practically does not change the wave kurtosis or the probability of the wave heights. In contrast, the presence of a long-wave system makes the waves more asymmetric and more extreme. The probability of large waves increases in the bimodal systems with a low-frequency component. The initial wave spectrum expands as a result of the wave interaction and tends to a quasistationary state. One may anticipate that the formulated conclusions are applicable beyond the limits of the Korteweg-de Vries equation to other kindred frameworks and corresponding phenomena. This work was supported by the Russian Science Foundation (project No. 18-77-00063).
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Kheyrkhah Shali, Roya, and Seyed Kamaledin Setarehdan. "The Effective Brain Areas in Recognition of Dyslexia." International Clinical Neuroscience Journal 7, no. 3 (June 21, 2020): 147–52. http://dx.doi.org/10.34172/icnj.2020.16.
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Background: The brain has four lobes consist of frontal, parietal, occipital, and temporal. Most researchers have reported that the left occipitotemporal region of the brain, which is the combined region of the occipital and temporal lobes, is less active in children with dyslexia like Sklar, Glaburda, Ashkenazi and Leisman. Methods: There are different methods and tools to investigate how the brain works, such as magnetic resonance imaging (MRI), positron emission tomography (PET), magneto-encephalography (MEG) and electroencephalography (EEG). Among these, EEG determines the electrical activity of the brain with the electrodes placed on the special areas on the scalp. In this research, we processed the EEG signals of dyslexic children and healthy ones to determine what the areas of the brain are most likely to cause the disease. Results: For this purpose, we extracted 43 features, including relative spectral power (RSP) features, mean, standard deviation, skewness, kurtosis, Hjorth, and AR parameters. Then an SVM classifier is used to separate two classes. Finally, we show the particular brain activation pattern by calculating the correlation coefficients and co-occurrence matrices, which suggests the activation of the working memory region as an active area. Conclusion: By identifying the brain areas involved in reading activity, it has expected that psychologists and physicians will be able to design the therapeutic exercises to activate this part of the brain.
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Miron, Mihaela, Simona Moldovanu, Bogdan Ioan Ștefănescu, Mihai Culea, Sorin Marius Pavel, and Anisia Luiza Culea-Florescu. "A New Approach in Detectability of Microcalcifications in the Placenta during Pregnancy Using Textural Features and K-Nearest Neighbors Algorithm." Journal of Imaging 8, no. 3 (March 19, 2022): 81. http://dx.doi.org/10.3390/jimaging8030081.
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(1) Background: Ultrasonography is the main method used during pregnancy to assess the fetal growth, amniotic fluid, umbilical cord and placenta. The placenta’s structure suffers dynamic modifications throughout the whole pregnancy and many of these changes, in which placental microcalcifications are by far the most prominent, are related to the process of aging and maturation and have no effect on fetal wellbeing. However, when placental microcalcifications are noticed earlier during pregnancy, they could suggest a major placental dysfunction with serious consequences for the fetus and mother. For better detectability of microcalcifications, we propose a new approach based on improving the clarity of details and the analysis of the placental structure using first and second order statistics, and fractal dimension. (2) Methods: The methodology is based on four stages: (i) cropping the region of interest and preprocessing steps; (ii) feature extraction, first order—standard deviation (SD), skewness (SK) and kurtosis (KR)—and second order—contrast (C), homogeneity (H), correlation (CR), energy (E) and entropy (EN)—are computed from a gray level co-occurrence matrix (GLCM) and fractal dimension (FD); (iii) statistical analysis (t-test); (iv) classification with the K-Nearest Neighbors algorithm (K-NN algorithm) and performance comparison with results from the support vector machine algorithm (SVM algorithm). (3) Results: Experimental results obtained from real clinical data show an improvement in the detectability and visibility of placental microcalcifications.
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Wang, Mingwei, Ziyin Wu, Fanlin Yang, Yue Ma, Xiao Wang, and Dineng Zhao. "Multifeature Extraction and Seafloor Classification Combining LiDAR and MBES Data around Yuanzhi Island in the South China Sea." Sensors 18, no. 11 (November 8, 2018): 3828. http://dx.doi.org/10.3390/s18113828.
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Airborne light detection and ranging (LiDAR) full waveforms and multibeam echo sounding (MBES) backscatter data contain rich information about seafloor features and are important data sources representing seafloor topography and geomorphology. Currently, to classify seafloor types using MBES, curve features are extracted from backscatter angle responses or grayscale, and texture features are extracted from backscatter images based on gray level co-occurrence matrix (GLCM). To classify seafloor types using LiDAR, waveform features are extracted from bottom returns. This paper comprehensively considers the features of both LiDAR waveforms and MBES backscatter images that include the eight feature factors of the LiDAR full waveforms (amplitude, peak location, full width half maximum (FWHM), skewness, kurtosis, area, distance, and cross-section) and the eight feature factors of MBES backscatter images (mean, standard deviation (STD), entropy, homogeneity, contrast, angular second moment (ASM), correlation, and dissimilarity). Based on a support vector machine (SVM) algorithm with different kernel functions and penalty factors, a new seafloor classification method that merges multiple features is proposed for a beneficial exploration of acousto-optic fusion. The experimental results of the seafloor classification around Yuanzhi Island in the South China Sea indicate that, when LiDAR waveform features are merged (using an Optech Aquarius system) with MBES backscatter image features (using a Sonic 2024) to classify three types of sands, reefs, and rocks, the overall accuracy is improved to 96.71%, and the kappa reaches 0.94. After merging multiple features, the classification accuracies of the SVM, genetic algorithm SVM (GA-SVM) and particle swarm optimization SVM (PSO-SVM) increase by an average of 9.06%, 3.60%, and 2.75%, respectively.
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Becker, Anton S., Soleen Ghafoor, Magda Marcon, Jose A. Perucho, Moritz C. Wurnig, Matthias W. Wagner, Pek-Lan Khong, Elaine YP Lee, and Andreas Boss. "MRI texture features may predict differentiation and nodal stage of cervical cancer: a pilot study." Acta Radiologica Open 6, no. 10 (October 2017): 205846011772957. http://dx.doi.org/10.1177/2058460117729574.
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Background Texture analysis in oncological magnetic resonance imaging (MRI) may yield surrogate markers for tumor differentiation and staging, both of which are important factors in the treatment planning for cervical cancer. Purpose To identify texture features which may predict tumor differentiation and nodal status in diffusion-weighted imaging (DWI) of cervical carcinoma Material and Methods Twenty-three patients were enrolled in this prospective, institutional review board (IRB)-approved study. Pelvic MRI was performed at 3-T including a DWI echo-planar sequence with b-values 40, 300, and 800 s/mm2. Apparent diffusion coefficient (ADC) maps were used for region of interest (ROI)-based texture analysis (32 texture features) of tumor, muscle, and fat based on histogram and gray-level matrices (GLM). All features confounded by the ROI size (linear model) were excluded. The remaining features were examined for correlations with histological differentiation (Spearman) and nodal status (Kruskal–Wallis). Hierarchical cluster analysis was used to identify correlations between features. A P value < 0.05 was considered statistically significant. Results Mean age was 55 years (range = 37–78 years). Biopsy revealed two well-differentiated, eight moderately differentiated, two moderately to poorly differentiated tumors, and five poorly differentiated tumors. Six tumors could not be graded. Lymph nodes were involved in 11 patients. Three GLM features correlated with the differentiation: LRHGE (ϱ = 0.53, P = 0.03), ZP (ϱ = –0.49, P < 0.05), and SZE (ϱ = –0.51, P = 0.04). Two histogram features, skewness (0.65 vs. 1.08, P = 0.04) and kurtosis (0.53 vs. 1.67, P = 0.02), were higher in patients with positive nodal status. Cluster analysis revealed several co-correlations. Conclusion We identified potentially predictive GLM features for histological tumor differentiation and histogram features for nodal cancer stage.
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Jin, Yinghua, Jiawei Xu, Hongshi He, Mai-He Li, Yan Tao, Yingjie Zhang, Rui Hu, et al. "The Changbai Alpine Shrub Tundra Will Be Replaced by Herbaceous Tundra under Global Climate Change." Plants 8, no. 10 (September 25, 2019): 370. http://dx.doi.org/10.3390/plants8100370.
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Significant replacement of shrub species by herbaceous species has been observed in the Changbai alpine tundra zone, China, since the 1990s. This study used plot surveys to analyze variations in the spatial distribution of dominant plants and to ascertain the changing mechanisms of dominant species in the alpine tundra zone. We found that the two previously dominant shrubs, Rhododendron chrysanthum and Vaccinium uliginosum, differed markedly in their distribution characteristics. The former had the highest abundance and the lowest coefficient of variation, skewness, and kurtosis, and the latter showed the opposite results, while the six herb species invaded had intermediate values. R. chrysanthum still had a relatively uniform distribution, while the herbaceous species and V. uliginosum had a patch distribution deviating from the normal distribution in the tundra zone. Micro-topography and slope grade had stronger effects on the spatial distribution of the eight plant species than elevation. Herbs tended to easily replace the shrubs on a semi-sunny slope aspect, steep slope, and depression. Overall, the dominance of dwarf shrubs declined, while the herbaceous species have encroached and expanded on the alpine tundra zone and have become co-dominant plant species. Our results suggest that various micro-topographic factors associated with variations in climatic and edaphic conditions determine the spatial distribution of plants in the alpine tundra zone. Future climate warming may cause decreased snow thickness, increased growing season length, and drought stress, which may further promote replacement of the shrubs by herbs, which shows retrogressive vegetation successions in the Changbai alpine tundra zone. Further studies need to focus on the physio-ecological mechanisms underlying the vegetation change and species replacement in the alpine tundra area under global climate change.
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Garcia-Benadi, Albert, Joan Bech, Sergi Gonzalez, Mireia Udina, Bernat Codina, and Jean-François Georgis. "Precipitation Type Classification of Micro Rain Radar Data Using an Improved Doppler Spectral Processing Methodology." Remote Sensing 12, no. 24 (December 16, 2020): 4113. http://dx.doi.org/10.3390/rs12244113.
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This paper describes a methodology for processing spectral raw data from Micro Rain Radar (MRR), a K-band vertically pointing Doppler radar designed to observe precipitation profiles. The objective is to provide a set of radar integral parameters and derived variables, including a precipitation type classification. The methodology first includes an improved noise level determination, peak signal detection and Doppler dealiasing, allowing us to consider the upward movements of precipitation particles. A second step computes for each of the height bin radar moments, such as equivalent reflectivity (Ze), average Doppler vertical speed (W), spectral width (σ), the skewness and kurtosis. A third step performs a precipitation type classification for each bin height, considering snow, drizzle, rain, hail, and mixed (rain and snow or graupel). For liquid precipitation types, additional variables are computed, such as liquid water content (LWC), rain rate (RR), or gamma distribution parameters, such as the liquid water content normalized intercept (Nw) or the mean mass-weighted raindrop diameter (Dm) to classify stratiform or convective rainfall regimes. The methodology is applied to data recorded at the Eastern Pyrenees mountains (NE Spain), first with a detailed case study where results are compared with different instruments and, finally, with a 32-day analysis where the hydrometeor classification is compared with co-located Parsivel disdrometer precipitation-type present weather observations. The hydrometeor classification is evaluated with contingency table scores, including Probability of Detection (POD), False Alarm Rate (FAR), and Odds Ratio Skill Score (ORSS). The results indicate a very good capacity of Method3 to distinguish rainfall and snow (PODs equal or greater than 0.97), satisfactory results for mixed and drizzle (PODs of 0.79 and 0.69) and acceptable for a reduced number of hail cases (0.55), with relatively low rate of false alarms and good skill compared to random chance in all cases (FAR < 0.30, ORSS > 0.70). The methodology is available as a Python language program called RaProM at the public github repository.
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Mohamed, Rania M., Bikash Panthi, Beatriz Adrada, Rosalind Candelaria, Mary S. Guirguis, Wei Yang, Medine Boge, et al. "Abstract P6-01-06: Multi-Parametric MRI-Based Radiomics Models from Tumor and Peritumoral Regions as Potential Predictors of Treatment Response to Neoadjuvant Systemic Therapy in Triple Negative Breast Cancer Patients." Cancer Research 83, no. 5_Supplement (March 1, 2023): P6–01–06—P6–01–06. http://dx.doi.org/10.1158/1538-7445.sabcs22-p6-01-06.
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Abstract PURPOSE Triple negative breast cancer (TNBC) is an aggressive and heterogeneous subtype of breast cancer. Pathologic complete response (pCR) to neoadjuvant systemic therapy (NAST) predicts better survival. Early prediction of the treatment response can potentially triage non-responding patients to alternative protocol treatments, spare them of the unneeded toxicity, and improve pCR. We evaluated the ability of radiomic textural analysis of intratumoral and peritumoral regions on the dynamic contrast enhanced (DCE) and diffusion-weighted imaging (DWI) MRI images obtained early during NAST to predict pCR. MATERIALS AND METHODS This IRB-approved prospective study (NCT02276443) included 182 patients with biopsy proven stage I-III TNBC who had multiparametric MRIs at baseline (BL), post 2 cycles (C2), and post 4 cycles (C4) of NAST before surgery. Tumors and peritumoral regions of 5 mm and 10 mm in thickness were segmented on the 2.5 minutes DCE subtraction images and on the b=800 DWI images. Ten histogram-based first order texture features including mean, minimum, maximum, standard deviation, kurtosis, skewness, 1st, 5th, 95th, and 99th percentile, and 300 radiomic Grey Level Co-occurrence matrix (GLCM) features along with their absolute and relative differences between the 3 imaging time points were extracted from the tumors and from the peritumoral regions with an in-house Matlab toolbox. Treatment response at surgery (pCR vs non-pCR) was documented. The samples were divided into training and testing datasets by a 2:1 ratio. Area under the receiver operating characteristics curve (AUC ROC) was calculated for univariate analysis in predicting pCR. Logistic regression with elastic net regularization was performed for texture feature selection. Parameter optimization was performed by using 5-fold cross-validation based on mean cross-validated AUC in the training set. RESULTS Of 182 TNBC patients, 88 (48%) had pCR and 94 (52%) did not achieve pCR. Eight multivariate models combining radiomic features from both DCE and DWI tumoral and peritumoral regions had AUC > 0.8 (0.807-0.831) with p-value < 0.001 in both training and testing sets. The highest AUC=0.831 was obtained from a model consisting of 15 radiomic features: tumor DWI (5 GLCM features) at C2, peritumoral region on DCE (skewness) at C2, tumor DCE (1st, 5th percentile) at C4, tumor DWI (3 GLCM features) at C4, peritumoral region DWI (1 GLCM feature) at C4, and the relative difference between C4/C2 on DCE (5th, 95th percentile and mean). CONCLUSION Multi-parametric MRI-based radiomics models from the tumor and the peritumoral regions showed high accuracy as potential early predictors of NAST response in TNBC patients. Citation Format: Rania M. Mohamed, Bikash Panthi, Beatriz Adrada, Rosalind Candelaria, Mary S. Guirguis, Wei Yang, Medine Boge, Miral Patel, Nabil Elshafeey, Sanaz Pashapoor, Zijian Zhou, Jong Bum Son, Ken-Pin Hwang, H. T. Carisa Le-Petross, Jessica Leung, Marion E. Scoggins, Gary J. Whitman, Zhan Xu, Deanna L. Lane, Tanya Moseley, Frances Perez, Jason White, Elizabeth Ravenberg, Alyson Clayborn, Mark Pagel, Huiqin Chen, Jia Sun, Peng Wei, Alastair M. Thompson, Stacy Moulder, Anil Korkut, Lei Huo, Kelly K. Hunt, Jennifer K. Litton, Vicente Valero, Debu Tripathy, Clinton Yam, Jingfei Ma, Gaiane Rauch. Multi-Parametric MRI-Based Radiomics Models from Tumor and Peritumoral Regions as Potential Predictors of Treatment Response to Neoadjuvant Systemic Therapy in Triple Negative Breast Cancer Patients [abstract]. In: Proceedings of the 2022 San Antonio Breast Cancer Symposium; 2022 Dec 6-10; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2023;83(5 Suppl):Abstract nr P6-01-06.
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Mohamed, Rania M., Bikash Panthi, Beatriz Adrada, Rosalind Candelaria, Mary S. Guirguis, Wei Yang, Medine Boge, et al. "Abstract P6-01-35: A Pre-operative Dynamic Contrast Enhanced MRI-Based Radiomics Models as Predictors of Treatment Response after Neoadjuvant Systemic Therapy in Triple Negative Breast Cancer Patients." Cancer Research 83, no. 5_Supplement (March 1, 2023): P6–01–35—P6–01–35. http://dx.doi.org/10.1158/1538-7445.sabcs22-p6-01-35.
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Abstract Background and Purpose Triple negative breast cancer (TNBC) is a biologically aggressive tumor and a refractory subtype of breast cancer due to the lack of therapeutic targets, such as estrogen receptors, progesterone receptors, and human epidermal growth factor receptor 2. In this study, we investigated the accuracy of radiomic models based on the dynamic contrast enhanced (DCE) MRI images obtained after the completion of NAST as discriminators of treatment response in TNBC patients. Materials and Methods This IRB-approved prospective study (ARTEMIS trial, NCT02276443) included 181 patients with biopsy proven stage I-III TNBC who Had MRIs after completion of NAST and before surgery. Patients were classified as pathologic complete response (pCR) and non-pCR at the surgery. Tumors were segmented on the 2.5 minutes DCE subtraction images. Regions with necrosis or clip artifacts were excluded from the contour. If tumors were not visible, the tumor bed was contoured. Whole-tumor histogram-based first order texture features (p=10) including mean, minimum, maximum, Standard deviation, kurtosis, skewness, 1st, 5th, 95th, and 99th percentiles, and radiomic (p=300) Grey Level Co-occurrence matrix (GLCM) features were extracted with an in-house Matlab toolbox. The samples were split into training and testing data sets by a 2:1 ratio. For univariate analysis area under the receiver operating characteristics curve (AUC ROC) was performed for pCR status prediction. For texture feature selection logistic regression with elastic net regularization was performed. Parameter optimization was performed by using 5-fold cross-validation based on mean cross-validated AUC in the training set. A P-value less than 0.05 was considered statistically significant. Results Of the total 181 patients, 88 (49%) had pCR and 93 (51%) had non-pCR. Univariate analysis identified 7 statistically significant first order imaging features (Minimum, Maximum, Mean, 1st Percentile, 5th Percentile, 95th Percentile, and 99th Percentile) with AUC >= 0.7 (p< 0.001), in both training and testing data sets. Percentile 5 showed highest AUC = 0.78 (p< 0.001). Two multivariate models were statistically significant at cross-validation with AUC>=0.7. The first model combined 2 first order data (Percentile 1 and Percentile 5) with AUC = 0.73 (p< 0.001). The second model combined 8 first order features (Percentile 1, 5, 95, 99, Mean, Minimum, Maximum, and Skewness) and 24 GLCM features with AUC = 0.7 (p=0.003). Conclusion DCE-MRI radiomic features from tumor and tumor bed regions in TNBC may be helpful imaging biomarkers for predicting treatment response after NAST. Citation Format: Rania M. Mohamed, Bikash Panthi, Beatriz Adrada, Rosalind Candelaria, Mary S. Guirguis, Wei Yang, Medine Boge, Miral Patel, Nabil Elshafeey, Sanaz Pashapoor, Zijian Zhou, Jong Bum Son, Ken-Pin Hwang, H. T. Carisa Le-Petross, Jessica Leung, Marion E. Scoggins, Gary J. Whitman, Zhan Xu, Deanna L. Lane, Tanya Moseley, Frances Perez, Jason White, Huiqin Chen, Jia Sun, Peng Wei, Jennifer K. Litton, Vicente Valero, Clinton Yam, Mark Pagel, Jingfei Ma, Gaiane Rauch. A Pre-operative Dynamic Contrast Enhanced MRI-Based Radiomics Models as Predictors of Treatment Response after Neoadjuvant Systemic Therapy in Triple Negative Breast Cancer Patients [abstract]. In: Proceedings of the 2022 San Antonio Breast Cancer Symposium; 2022 Dec 6-10; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2023;83(5 Suppl):Abstract nr P6-01-35.
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Alberghi, S., A. Maurizi, and F. Tampieri. "Relationship between the Vertical Velocity Skewness and Kurtosis Observed during Sea-Breeze Convection." Journal of Applied Meteorology 41, no. 8 (August 2002): 885–89. http://dx.doi.org/10.1175/1520-0450(2002)041<0885:rbtvvs>2.0.co;2.
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El-Omari, Hussein Abdulla. "Marketing information, management and use: the case of Saudi Arabia." Journal of Islamic Marketing 10, no. 2 (June 10, 2019): 653–72. http://dx.doi.org/10.1108/jima-06-2017-0071.
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Purpose Obtaining, managing and using proper marketing information are considered an important strategic issue that cannot be ignored in the light of stiffening competition locally and internationally. Therefore, the purpose of this study is to examine the level of importance attached by Saudi industrial organizations to good management and use of quality marketing information. Design/methodology/approach A questionnaire method was used to collect the required data. Using a self-distributed method, the questionnaire was provided to top management levels of 80 companies from different industrial sectors in Saudi Arabia, selected randomly from a list provided by Saudi’s Chamber of Industry. Despite all attempts, only 30 completed questionnaires were returned and used in the statistical analysis for this study. This gave a response rate of 37.5 per cent. Used in this study’s statistical analysis were descriptive statistics such as frequencies, measures of central tendency such as the mean and median, measures of dispersion such as standard deviation and measures of distribution such as skewness and kurtosis. Advanced statistics, such as factor analysis statistics, were also used. Findings The study’s findings indicate how company variables are related to the ideal and actual marketing information application variables. Management’s capacity to develop a marketing plan and effectively observe the improvement may be the most demanding part of achieving desired results. This study further examined the degree to which Saudi business organizations are aware of how important it is to obtain and use proper marketing information. To develop good marketing plans, those business organizations must understand the nature of Saudi’s social structure. Its organization and welfare services are rooted in the values and traditions of Arab Muslim Culture. One of the five basic Pillars of Faith in Islam is the practice of Alms-giving and care of needy. Furthermore, people’s behavior is heavily influenced by the value, norms and expectations of Islam. Research limitations/implications This research offers a methodology to develop a better comprehension of the importance of having good management of marketing information and its use in Saudi Arabia via a description of the significant variables that form marketing information management and use. The current study also calls for more empirical research into this area of marketing in Saudi Arabia. The empirical nature of this study revealed some recommendations for future work that should look into the issues highlighted in this study. It would be useful to apply this study to other similar contexts, which may prove helpful in reexamining the validity of its results. However, further studies are needed to validate the findings of this study, as all behavioral and cultural variables were not investigated and are left for future research. In addition, this is a deductive research; therefore, some important variables may have been omitted, which is another reason for recommending more empirical studies of this type in Saudi Arabia and similar contexts. Practical implications Investigating this type of study in Saudi Arabia gives a unique implication, as it calls for better understanding of the Islamic Marketing Environment of this country, which has two important holy Mosques of Islam (i.e. Al-Haram and the Prophet’s Mosques). There is no denying that the marketing environment characteristics in any society are affected by environmental circumstances, and Saudi Arabia as the most important Muslim Country, is no exception. Originality/value The central issue of this paper is related to the importance of having, managing and using good marketing information by industrial organizations. With this issue in mind, this study was carried out in a Muslim country (i.e. Saudi Arabia). Although the Saudi market has many dealers, domestic and international trades and co-operatives, there is little relevant data about the existing marketing systems, i.e. scarcity of market data and information concerning demand, consumption, opportunities and competition.
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Ferrer Lores, Blanca, Irene Mayorga-Ruiz, Angel Alberich Bayarri, Daniella Morello-González, Irene Pastor-Galán, Blanca Navarro-Cubells, Alicia Serrano, et al. "Prognostic Value of Radiomics Signature By Diagnostic 18F-FDG PET/CT Analysis in Aggressive Non-Hodgkin's Lymphoma." Blood 132, Supplement 1 (November 29, 2018): 1703. http://dx.doi.org/10.1182/blood-2018-99-119851.
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Abstract BACKGROUND Although the overall prognosis of patients with aggressive non-Hodgkin's lymphoma (NHL) has improved, nearly a third of patients will have refractory disease or relapse. Identification of these high-risk patients using traditional prognostic factors is limited. PET is the recommended imaging modality for the staging of FDG-avid lymphoma but the value of a comprehensive new imaging biomarkers analysis applied to PET for the prediction of patients outcome has still not been deeply investigated. New metrics estimating the overall tumor burden such as metabolic tumor volume (MTV) and those that may capture intratumoral biological heterogeneity such as total lesion glycolysis (TLG) have been used to predict progression-free survival. AIM The goal of the present work was to characterize Lymphoma lesions by extracting several metabolic volume and textural properties as radiomics features and evaluate their performance as surrogate indicators of the number of treatment cycles, and treatment response. Materials and methods In this retrospective, observational study, we included aggressive non-Hodgkin's lymphoma patients consecutively diagnosed according to the WHO 2016 between January of 2015 to December of 2017. A diagnostic PET/CT scan were essential. 1 patient without treatment was excluded. Clinical and biological data were extracted from medical records. PET/CT examinations were exported from the PACS and loaded into QUIBIM Precision 2.3 analysis platform (QUIBIM, Valencia, Spain) for the calculation of metabolic volumes and textural properties. The SUV values of the PET images were normalized to the average liver SUV, and the lesions were automatically segmented considering a threshold of 41% of the maximum SUV (SUVmax). Physiological uptakes in organs and tissues like bowel, bladder, brain, among others, were manually removed. In the lesions volumetry analysis, the metabolic tumor volume (MTV) and total lesion glycolysis (TLG) were calculated. For the extraction of texture features, first order histogram descriptors (SUV values distribution, skewness, kurtosis) as well as second order descriptors were extracted after computing the Gray-Level Co-Occurrence Matrix (GLCM). For the statistical analysis, the Z-score of all imaging features obtained was calculated and a multi-variate analysis was performed by first calculating the intra-class correlation (ICC) to reduce redundant variables. Second, data hierarchy clustering was applied to automatically obtain patient groups according to different imaging signatures. The prognostic performance of IPI with and without the imaging signature was evaluated by a Discriminant Analysis for the number of treatment cycles and treatment response. Prognostic value of OS was performed through Kaplan-Meier analysis. Results A total of 41 patients were included. The descriptive analysis of patients recruited with demographic and clinical data can be appreciated in Table 1. Radiomics features extracted allowed to clusterize patients in different groups that were later introduced in the classifier (Figure 1). The classifier based on discriminant model including the IPI factors predicted number of treatment cycles with a 65.9% of accuracy, being the age the factor with the highest weight (0.818). Adding information about imaging features from PET increased the accuracy to 86.5%. For the treatment response assessment, the IPI factors predicted response correctly in 71.4% of cases, being ECOG the parameter with the highest weight (0.974). Prediction was fully accurate when adding the imaging features, with a 100% of accuracy. The texture feature with the highest importance was 'dissimilarity' of the pixels (weight of 15.919). Conclusion The addition of radiomics features to the conventional IPI evaluation of patients allows for a significant increase in predictive performance, both for determining which patients will have more than 1 treatment lines and those who will respond to treatment. The results of this study would have an impact in disease management with a combined IPI and radiomics-based prognostic evaluation of patients at diagnosis. Disclosures No relevant conflicts of interest to declare.
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Langsen, Michael Laurence, Jerry Thwin Wong, Maximilian Merz, Brian Yu, Hemn Mohammadpour, Philip L. McCarthy, and Jens Hillengass. "Prediction of Malignant Cell Infiltration Patterns with Texture Features of Biopsy-Correlated Positron Emission Tomography of Osteolytic Lesions in Multiple Myeloma." Blood 138, Supplement 1 (November 5, 2021): 3997. http://dx.doi.org/10.1182/blood-2021-152846.
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Abstract Introduction: Osteolytic bone disease represents a severe clinical presentation of multiple myeloma (MM), as MM cells infiltrate the bone marrow throughout the skeleton, forming tumors, activating bone-resorbing osteoclasts, and impeding bone-depositing osteoblast activity. The extent of osteolytic lesions encompassing the skeleton influences the severity of disease for patients, with severe lesions leading to skeletal-related events (SREs). MM cell infiltration into the bone marrow gradually increases with disease progression; however, the distribution of myeloma cells within the bone and marrow is heterogenous. Standard of care imaging modalities used to monitor the number, size, and extent of bone destruction of lesions include whole-body magnetic resonance imaging (MRI), whole-body low-dose computed tomography (CT), and positron emission tomography with CT (PET/CT). With the use of computational methodologies, imaging textural features can be calculated from PET. In this prospective study, we aim to find PET textural features that will correlate with histological evaluation of myeloma cell infiltration patterns of osteolytic lesions and un-guided bone marrow biopsies in MM patients. Patients and Methods: 40 patients were enrolled in this prospective study with either newly diagnosed multiple myeloma (n=16, 40%) or relapse/refractory multiple myeloma (n= 24, 60%). Whole-body PET/CT imaging was performed on all patients as part of MM standard of care, after which CT-guided biopsies were taken from patient osteolytic lesions identified by imaging and in bone marrow biopsies. 68 biopsies were evaluated by an expert hematopathologist, of which 59 had analyzable imaging studies in the respective areas. Myeloma cell infiltration patterns in all biopsies were classified as interstitial (n=23, 41.8%), nodular (n=19, 34.6%), or packed (n=13, 23.6%), as described by Andrulis et al. (2014). PET lesion and bone marrow segmentation was performed on the Medical Imaging Interaction Toolkit (MITK) and 72 textural features were calculated using the PyRadiomics extension (van Griethuysen et al. 2017) for 3D Slicer 4.11 (Fedorov et al. 2012). PET quantitative features were calculated using the PET IndiC extension from 3D Slicer (QIICR (2015b)). Statistical analysis was performed via GraphPad Prism 9 (GraphPad Software, San Diego, California USA) and the Radiomics Analysis with R (RadAR, Benelli et al. 2020) package in the RStudio environment (RStudio Team (2020). RStudio: Integrated Development for R. RStudio, PBC, Boston, MA). Results: Lesion biopsy samples predominantly favored packed infiltration pattern (53.6%) over interstitial (21.4%) or nodular (25%), while staging/follow-up bone marrow biopsies predominantly favored interstitial or nodular patterns (47.5% and 35.0%, respectively) compared to packed (22.5%). Two-way analysis of variance (ANOVA) tests was performed to compare imaging features between biopsy infiltration patterns and also between disease status of patients. The first order uniformity (p<0.05) was able to discern between interstitial, nodular, or packed infiltration for all biopsies and textural features from the Gray Level Co-Occurrence Matrix (GLCM) Correlation and Joint Entropy were accurately able to discern between interstitial and either nodular or packed infiltration patterns for all biopsies (p<0.01). Within lesion biopsies specifically (n=25), first order uniformity, kurtosis, skewness, and textural features Joint Entropy and Dependence Non-Uniformity could discern between MM cell infiltration patterns (p<0.05). PET quantitative SUV statistics did not show any significant separation between infiltration patterns. First order, textural, and PET SUV quantitative features could not distinguish disease status of the patient. Conclusions: In this preliminary study, we demonstrate a correlation between textural imaging features and pathological findings within the bone marrow and osteolytic lesions of MM patients. This correlation illustrates a potential link between computational imaging features to predict pathological findings in patients with MM. Further expansion of this study with genomic, flow cytometry, and multimodality imaging data could lead to the generation of computational modeling of the pathophysiology of osteolytic lesions in MM and a reduction in the need for invasive bone marrow and lesion biopsies. Figure 1 Figure 1. Disclosures Merz: Takeda: Honoraria; onkowissen.de: Honoraria; Amgen: Honoraria; BMS: Honoraria; Celgene: Honoraria; Sanofi: Honoraria; Janssen: Honoraria; GSK: Honoraria; Hexal: Honoraria. McCarthy: Janssen: Honoraria, Membership on an entity's Board of Directors or advisory committees; Bluebird: Honoraria, Membership on an entity's Board of Directors or advisory committees; Bristol Myers Squibb: Honoraria, Membership on an entity's Board of Directors or advisory committees; Juno: Honoraria, Membership on an entity's Board of Directors or advisory committees; Karyopharm: Honoraria, Membership on an entity's Board of Directors or advisory committees; Celgene: Honoraria, Membership on an entity's Board of Directors or advisory committees; Magenta Therapeutics: Honoraria, Membership on an entity's Board of Directors or advisory committees; Oncopeptides: Honoraria, Membership on an entity's Board of Directors or advisory committees; Takeda: Honoraria, Membership on an entity's Board of Directors or advisory committees. Hillengass: Beijing Life Oasis Public Service Center: Speakers Bureau; Oncopeptides: Membership on an entity's Board of Directors or advisory committees; Axxess Network: Membership on an entity's Board of Directors or advisory committees; Beijing Medical Award Foundation: Speakers Bureau; Adaptive: Membership on an entity's Board of Directors or advisory committees; Bristol Myers Squibb: Membership on an entity's Board of Directors or advisory committees; Skyline: Membership on an entity's Board of Directors or advisory committees; GlaxoSmithKline: Membership on an entity's Board of Directors or advisory committees; Sanofi: Membership on an entity's Board of Directors or advisory committees; Oncotracker: Membership on an entity's Board of Directors or advisory committees; Curio Science: Speakers Bureau.
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Alroy, John. "New methods for quantifying macroevolutionary patterns and processes." Paleobiology 26, no. 4 (2000): 707–33. http://dx.doi.org/10.1666/0094-8373(2000)026<0707:nmfqmp>2.0.co;2.
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This paper documents a series of methodological innovations that are relevant to macroevolutionary studies. The new methods are applied to updated faunal and body mass data sets for North American fossil mammals, documenting several key trends across the late Cretaceous and Cenozoic. The methods are (1) A maximum likelihood formulation of appearance event ordination. The reformulated criterion involves generating a maximally likely hypothesized relative ordering of first and last appearances (i.e., an age range chart). The criterion takes faunal occurrences, stratigraphic relationships, and the sampling probability of individual genera and species into account. (2) A nonparametric temporal interpolation method called “shrink-wrapping” that makes it possible to employ the greatest possible number of tie points without violating monotonicity or allowing abrupt changes in slopes. The new calibration method is used in computing provisional definitions of boundaries among North American land mammal ages. (3) Additional methods for randomized subsampling of faunal lists, one weighting the number of lists that have been drawn by the sum of the square of the number of occurrences in each list, and one further modifying this approach to account for long-term changes in average local species richness. (4) Foote's new equations for instantaneous speciation and extinction rates. The equations are rederived and used to generate time series, confirm that logistic dynamics result from the diversity dependence of speciation but not extinction, and define the median duration of species (i.e., 2.6 m.y. for Eocene-Pleistocene mammals). (5) A method employing the G likelihood ratio statistic that is used to quantify the volatility of changes in the relative proportion of species falling in each of several major taxonomic groups. (6) Univariate measures of body mass distributions based on ordinary moment statistics (mean, standard deviation, skewness, kurtosis). These measures are favored over the method of cenogram analysis. Data are presented showing that even diverse individual fossil collections merely yield a noisy version of the same pattern seen in the overall continental data set. Peaks in speciation rates, extinction rates, proportional volatility, and shifts in body mass distributions occur at different times, suggesting that environmental perturbations do not have simple effects on the biota.
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Kostakoglu, Lale, Paola Berchialla, Federico Dalmasso, Larry A. Pierce, Umberto Vitolo, Maurizio Martelli, Laurie H. Sehn, et al. "A Prognostic Model Integrating PET-Derived Quantitative Parameters and Image Texture Analyses Using Radiomics in a Large Prospective Phase III Trial, GOYA." Blood 134, Supplement_1 (November 13, 2019): 883. http://dx.doi.org/10.1182/blood-2019-123450.
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Introduction: Our objective was to develop a prognostic model that predicts progression-free survival (PFS) and overall survival (OS) to enable risk-adapted strategies in patients with previously untreated diffuse large B-cell lymphoma (DLBCL). We retrospectively investigated the value of quantitative image texture features (i.e. 'radiomics' evaluating tumor heterogeneity) using FDG PET/CT data sets in a large, prospective Phase III trial, GOYA (NCT01287741). Methods: In the GOYA trial, which compared obinutuzumab versus rituximab both in combination with CHOP chemotherapy, there was no significant treatment effect between the two arms, thus the two arms were combined for this study. Baseline PET/CT images with regions of interests (ROIs) defined by qualified physicians were analyzed for radiomics features. Image texture features (ITF) were computed using the open-source and validated PET Oncology Radiomics Test Suite (PORTS). The clinical risk factors (International Prognostic Index [IPI], Ann Arbor stage, extranodal disease, bulky disease), cell of origin (COO), standard PET-derived metrics (standard uptake value [SUV]-mean, SUV-max, total metabolic tumor volume [TMTV], total lesion glycolysis [TLG]), SUV histogram metrics (variance, skewness, and kurtosis), and ITF were evaluated for prediction of PFS and OS. TMTV was estimated using adaptive thresholding. Prognostic models were generated by means of multivariate Cox regression analysis, modeling PFS, and OS. In the absence of an independent patient cohort for external model validation, an internal validation, based on c-index and Brier score, was carried out using bootstrap resampling methods. Stratification of patients into risk groups was achieved through maximally selected rank statistics. Multivariate analysis was also carried out on a subgroup of patients with available COO information. Results: The median follow-ups for PFS and OS were 46 and 50 months, respectively. Baseline PET scans were available for 1334 patients with detectable lesions, and 1077 baseline scans were evaluable for calculating ITFs. In the univariate analysis, high TMTV, histogram mean, histogram variance, and the ITFs gray-tone spatial dependence matrices (GTSDM) difference entropy and low gray-level zone length matrix (GLSZM) small zone high gray emphasis were risk factors for PFS, while high TMTV, histogram mean, and the ITF GTSDM inverse difference moment were risk factors for OS (Table 1, showing 95% CI, HR, and p-values for both univariate and multivariate analyses). In multivariate analysis, the risk factors included IPI, Ann Arbor stage, high TMTV, histogram mean, and GTSDM inverse difference moment; results were generally consistent in the multivariate subgroup analysis on patients with COO data available (Table 1). Based on the multivariate model, the probabilities for PFS and OS at 2 and 4 years for individual patients were established (Table 2). By combining TMTV (four categorical groups) with ITF, COO, and predictive clinical factors, three prognostic subgroups of treatment failure risk were identified: low (55% of patients), intermediate (34%), and high (11%). Hazard ratios for high and intermediate risk compared with low risk were 2.16 (p<0.001) and 1.17 (p=0.004) for PFS, and 3.82 (p<0.001) and 1.85 (p<0.001) for OS. The corresponding probability of survival at 2-years for high, intermediate and low risk groups were 87%, 82%, and 75% for PFS, and 94%, 90%, and 82% for OS. The 4-year survival probabilities were 83%, 77%, and 68% for PFS, and 91%, 86%, and 75% for OS (Table 2). For PFS, the accuracy of the Cox model was 0.63 with clinical variables only, 0.65 with the addition of TMTV, and 0.69 with the addition of ITFs; for OS, the corresponding values were 0.63, 0.65, and 0.70. Conclusion: A model including PET-derived quantitative ITF, in addition to significant clinical features, was able to predict survival probability for untreated DLBCL patients with good precision. The proposed PET-based prognostic model may help identify patients who could benefit from risk-adapted treatment modifications or novel approaches. Acknowledgments: GOYA was sponsored by F. Hoffmann-La Roche Ltd. Third-party editorial assistance, under the direction of Lale Kostakoglu, was provided by Katie Smith of Gardiner-Caldwell Communications and was funded by F. Hoffmann-La Roche Ltd. Disclosures Kostakoglu: F. Hoffman-La Roche: Consultancy; Genentech: Consultancy. Dalmasso:I-See s.r.l.: Employment. Pierce:Precision Sensing LLC: Equity Ownership. Vitolo:Janssen: Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Abbvie: Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Novartis: Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Celgene: Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Juno Therapeutics: Membership on an entity's Board of Directors or advisory committees; F. Hoffmann-La Roche: Speakers Bureau; Gilead: Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Kite: Membership on an entity's Board of Directors or advisory committees, Speakers Bureau. Martelli:Servier: Honoraria; F. Hoffman-La Roche, Celgene, Janssen, Sandoz, Novartis, Gilead: Honoraria, Membership on an entity's Board of Directors or advisory committees; F. Hoffman-La Roche, Celgene, Janssen, Sandoz, Novartis, Gilead: Honoraria, Membership on an entity's Board of Directors or advisory committees; Servier: Honoraria. Sehn:Janssen-Ortho: Consultancy, Honoraria; Janssen-Ortho: Honoraria. Trněný:Takeda: Consultancy, Honoraria; Gilead Sciences: Consultancy, Honoraria; F. Hoffmann-La Roche: Consultancy, Honoraria; Amgen: Consultancy, Honoraria; MorphoSys: Consultancy, Honoraria; Celgene: Consultancy; Bristol-Myers Squibb: Consultancy, Honoraria; Janssen: Consultancy, Honoraria; Incyte: Consultancy, Honoraria; Abbvie: Consultancy, Honoraria. Nielsen:F. Hoffmann-La Roche Ltd: Employment, Equity Ownership. Bolen:Genentech, Inc.: Employment; F. Hoffmann-La Roche: Equity Ownership. Sahin:F. Hoffmann-La Roche Ltd: Employment, Equity Ownership. Lee:Genentech: Employment; F. Hoffman-La Roche: Equity Ownership. El-Galaly:Roche: Employment, Other: Travel support; Takeda: Other: Travel support. Mattiello:F. Hoffmann-La Roche Ltd: Employment. Kinahan:Co-founded PET/X LLC: Equity Ownership; Philips Medical: Research Funding; GE Healthcare: Research Funding; F. Hoffmann-La Roche: Consultancy. Chauvie:International Agency on Atomic Energy (IAEA): Consultancy; Co-owner of Dixit srl (spin-off University of Torino): Equity Ownership; F. Hoffmann-La Roche: Research Funding; Fondazione Cassa di Risparmio di Cuneo (CRC): Research Funding; Italian Foundation on Lymphoma (FIL): Research Funding; Italian Association for Cancer Research (AIRC): Research Funding; SIRTEX: Speakers Bureau.
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Heaney, Richard A., Yihui Lan, and Sirimon Treepongkaruna. "Are Co-Skewness and Co-Kurtosis Factors Priced?" SSRN Electronic Journal, 2011. http://dx.doi.org/10.2139/ssrn.1917084.
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Liu, Zhi, Junwei Liu, and Kent Wang. "Measuring Co-Skewness and Co-Kurtosis Using Noisy High Frequency Financial Data." SSRN Electronic Journal, 2013. http://dx.doi.org/10.2139/ssrn.2277667.
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Christoffersen, Peter, Mathieu Fournier, Kris Jacobs, and Mehdi Karoui. "Option-Based Estimation of the Price of Co-Skewness and Co-Kurtosis Risk." SSRN Electronic Journal, 2015. http://dx.doi.org/10.2139/ssrn.2656412.
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"ICA Model of Densities for Images (ICADGGDD)." International Journal of Engineering and Advanced Technology 9, no. 1 (October 30, 2019): 5335–39. http://dx.doi.org/10.35940/ijeat.a2969.109119.
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In data analysis we use ICA is a basic tool, the aim is that find out a co-ordinate system where are the components of the data are independent.Mostly ICA method such as fastICA and Jointapproximation and diogonalization of eigen matrix (JADE), uses kurtosis as a metric of non gaussianity. But the assumption of kurtosis (fourth order cumulant) may not always satisfies practically. So there are one possible solution is to use skewness (third order cumulant) instead of kurtosis. In this paper we are going to introduce ICA based method, that approach is good for heavy-tailed (fourth order kurtosis) as well as asymmetric data (third order skewness).
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Ajili, Souad. "Size and Book to Market Effects vs. Co-skewness and Co-kurtosis in Explaining Stock Returns." SSRN Electronic Journal, 2004. http://dx.doi.org/10.2139/ssrn.634684.
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Li, Xintong, Matthew P. Martens, and Wolfgang Wiedermann. "Conditional Direction of Dependence Modeling: Application and Implementation in SPSS." Social Science Computer Review, February 28, 2022, 089443932110731. http://dx.doi.org/10.1177/08944393211073168.
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Conditional Direction Dependence Analysis (CDDA) has recently been proposed as a statistical framework to test reverse causation ( x → y vs. y → x) and potential of confounding ( x ← c → y) of variable relations in linear models when moderation is present. Similar to standard DDA, CDDA assumes that the “true” predictor is a continuous, non-normal, exogenous variable. Under non-normality, a conditional causal effect of one variable does not only change means, variances, and covariances, but also the distributional shape (i.e., skewness, kurtosis, co-skewness, and co-kurtosis) of another variable given the moderator. Such distributional changes can be used to study underlying mechanisms of heterogenous causal effects. The present study introduces conditional direction of dependence modeling and presents SPSS macros to make CDDA easily accessible to applied researchers. A real-world data example from the field of gambling addiction research is used to introduce the functionality of CDDA SPSS macros. Limitations of CDDA due to violated assumptions and poor data quality are discussed. The CDDA installation package is available at no charge from www.ddaproject.com .
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Agussationo, Yudhi, Indah Soesanti, and Warsun Najib. "Klasifikasi Citra X-Ray Diagnosis Tuberkulosis Berbasis Fitur Statistis." Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) 2, no. 3 (November 6, 2018). http://dx.doi.org/10.29207/resti.v2i3.523.
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Tuberkulosis merupakan salah satu penyebab kematian manusia. Hasil pemeriksaan x-ray diagnosis tuberkulosis dapat dijadikan objek pada proses ekstraksi ciri yang merupakan suatu tahapan dalam mengekstrak ciri/ informasi dari objek yang terdapat pada suatu citra diagnosis tuberkulosis. Pada penelitian ini digunakan metode ekstraksi ciri citra berbasis tekstur statistis orde satu (histogram), orde kedua berbasis Gray-Level Co-occurrence Matrix (GLCM), serta Principle Component Analysis (PCA). Data penelitian diperoleh dari RS Dr. Sardjito Yogyakarta sebanyak 33 citra digital x-ray pasien diagnosis tuberkulosis Tahun 2012 masing-masing 6 citra PA (Postero-Anterior) normal, 19 citra abnormal, 4 citra AP (Antero-Posterior) normal, dan 4 citra AP abnormal. Penelitian ini bertujuan mencari ciri terbaik yang terkandung pada citra x-ray diagnosis tuberkulosis menggunakan analisis tekstur statistis yang diperoleh dari fitur ciri yang terdapat pada metode ekstraksi ciri berbasis tekstur. Fitur ciri yang teridentifikasi antara lain: varians, std deviasi, skewness, kurtosis, contrast dan energy. Klasifikasi menggunakan data input berupa 33 data uji yang dibangun dengan metode Multi Layer Perceptron (MLP), sedang output berupa citra normal dan citra abnormal. Hasil penelitian menunjukkan bahwa akurasi klasifikasi menggunakan metode Histogram (81,81%), metode GLCM (96,96%), metode PCA (81,82%), dan metode kombinasi Histogram GLCM (100%).
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CAN, Ali, and Hasan ÖZSOY. "A Different Perspective on Air Pollution Measurements." Journal of Polytechnic, January 30, 2023. http://dx.doi.org/10.2339/politeknik.1126580.
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This study aims to determine the air pollution in Karabük province. For this purpose, a new equipment has been designed. The equipment can measure the SO2, CO2, CO, CH4, NOX, O3, PM2.5, and VOC pollution alongside with many atmospheric parameters. The measurement period has been decided to be one year starting from June 2021. The measurement period was one year, starting from June 2021. The measurements were taken at fifty points with 8 portable intermittent equipment. Then hourly and monthly averages were calculated. The calculation of the averages depends on many statistical analyses. The mean (geometric, harmonic, root, interquartile, Winsorized), median, midrange, Skewness, and Kurtosis analyses were done to obtain correct daily, and monthly averages. These analyses are necessary to comment on the intermittent measurement averages. The analyses of the collected data showed that the concentrations are changing considerably through the measurement period. The highest concentration was observed for the SO2, CO, NOX, and PM2.5 with respective values of 186.4, 170, 204.9, and 265 µg/m3. All these values are dangerous for human health. Elevation, temperatures, atmospheric pressure, and wind are sensitive parameters for atmospheric pollution. In Karabük province, most of the measurement points are affected by multi-pollution sources. The scatter diagrams also support this fact. During winter months, the pollution increases instantly. However, O3 and VOC parameters show different trends as compared to other pollutants. The concentration of these two parameters, namely O3 and VOC, increases during spring months. The O3 and VOC increase by 78.1%, and 43.2%, respectively due to photochemical reactions in the atmosphere in spring.
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Vijithananda, Sahan M., Mohan L. Jayatilake, Badra Hewavithana, Teresa Gonçalves, Luis M. Rato, Bimali S. Weerakoon, Tharindu D. Kalupahana, Anil D. Silva, and Karuna D. Dissanayake. "Feature extraction from MRI ADC images for brain tumor classification using machine learning techniques." BioMedical Engineering OnLine 21, no. 1 (August 1, 2022). http://dx.doi.org/10.1186/s12938-022-01022-6.
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Abstract Background Diffusion-weighted (DW) imaging is a well-recognized magnetic resonance imaging (MRI) technique that is being routinely used in brain examinations in modern clinical radiology practices. This study focuses on extracting demographic and texture features from MRI Apparent Diffusion Coefficient (ADC) images of human brain tumors, identifying the distribution patterns of each feature and applying Machine Learning (ML) techniques to differentiate malignant from benign brain tumors. Methods This prospective study was carried out using 1599 labeled MRI brain ADC image slices, 995 malignant, 604 benign from 195 patients who were radiologically diagnosed and histopathologically confirmed as brain tumor patients. The demographics, mean pixel values, skewness, kurtosis, features of Grey Level Co-occurrence Matrix (GLCM), mean, variance, energy, entropy, contrast, homogeneity, correlation, prominence and shade, were extracted from MRI ADC images of each patient. At the feature selection phase, the validity of the extracted features were measured using ANOVA f-test. Then, these features were used as input to several Machine Learning classification algorithms and the respective models were assessed. Results According to the results of ANOVA f-test feature selection process, two attributes: skewness (3.34) and GLCM homogeneity (3.45) scored the lowest ANOVA f-test scores. Therefore, both features were excluded in continuation of the experiment. From the different tested ML algorithms, the Random Forest classifier was chosen to build the final ML model, since it presented the highest accuracy. The final model was able to predict malignant and benign neoplasms with an 90.41% accuracy after the hyper parameter tuning process. Conclusions This study concludes that the above mentioned features (except skewness and GLCM homogeneity) are informative to identify and differentiate malignant from benign brain tumors. Moreover, they enable the development of a high-performance ML model that has the ability to assist in the decision-making steps of brain tumor diagnosis process, prior to attempting invasive diagnostic procedures, such as brain biopsies.
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Ghasemi, Abbas, Sangsig Yun, and Xianguo Li. "Fractal structures arising from interfacial instabilities in bio-oil atomization." Scientific Reports 11, no. 1 (January 11, 2021). http://dx.doi.org/10.1038/s41598-020-80059-w.
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AbstractThe intriguing multi-scale fractal patterns ubiquitously observed in nature similarly emerge as fascinating structures in two-phase fluid flows of bio-oil breakup and atomization processes. High-resolution microscopy of the two-phase flows under 15 flow conditions (cases of different flow rates of the liquid and co-flowing air streams as well as different degrees of liquid preheating) reveal that the geometrical complexities evolve under the competing/combined action of the instability mechanisms such as Kelvin–Helmholtz, Rayleigh–Taylor and Rayleigh–Plateau leading into the transition from break-up to atomization. A thorough analysis of the higher order moments of statistics evaluated based on the probability density functions from 15,000 fractal dimension samples suggest that a single-value analysis is not sufficient to describe the complex reshaping mechanisms in two-phase flows. Consistently positive skewness of the statistics reveal the role of abrupt two-phase mechanisms such as liquid column rupture, ligament disintegration, liquid sheet bursting and droplet distortions in a hierarchical geometrical entanglement. Further, large kurtosis values at increased flow inertia are found associated with turbulence-induced intermittent geometrical reshaping. Interestingly, the proposed power-law correlation reveals that the global droplet size obtained from laser-diffraction measurements declines as the two-phase geometrical complexity increases.
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Onwuegbuchulam, C. O., V. N. Nwugha, D. O. Ikoro, A. C. Ezebunanwa, and K. O. Osibe. "Determination of the Depositional Environment of Outcrop Section at Odoro Ikpe South Eastern Nigeria Using Pebble Morphometry." Journal of Geography, Environment and Earth Science International, April 25, 2019, 1–12. http://dx.doi.org/10.9734/jgeesi/2019/v21i130115.
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The depositional Environment of Odoro -Ikpe, South East Nigeria was interpreted using pebble morphometry and sieve analysis. A field observation, sieve analysis and pebble morphometric analysis was carried out on the area which comprises of conglomerate, pebbles, sand stone and intercalation of shale's and clay. The lithofacies observed are lateralitic layer, pebbly sand, alternating layers of sand and conglomerates and layers of massive sand stone. The graphic mean and skewness of the grain size analysis shows that the sediments are very coarse which indicates a high energy environment. Graphic standard deviation gives a clue that the sediment were very poorly sorted, which is indicative of a fluvial deposit with high energy , while kurtosis result revealed very coarse sediment deposits. This implies that the particles were not transported very far. Pebble analysis shows the geometric forms and this was deduced from the plot on shape measurement triangle. The shape is compact bladed which is common with river deposits. The bivarate plot of mean diameter against standard deviation also indicated a fluvial environment as well as bivarate plots of co-efficient of flatness against sphericity and maximum projection sphericity against oblate, prolate index which comprises of river and beach environment. All results showed or indicated that the environment of deposition was of river.
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Korkmaz, Mustafa Ç., Christophe Chesneau, and Zehra Sedef Korkmaz. "The Unit Folded Normal Distribution: A New Unit Probability Distribution with the Estimation Procedures, Quantile Regression Modeling and Educational Attainment Applications." Journal of Reliability and Statistical Studies, April 26, 2022. http://dx.doi.org/10.13052/jrss0974-8024.15111.
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In this paper, we develop a continuous distribution on the unit interval characterized by the distribution of the absolute hyperbolic tangent transformation of a random variable following the normal distribution. The lack of research on the prospect of hyperbolic transformations providing flexible distributions on the unit interval is a motivation for the study. First, we study it theoretically and discuss its properties of interest from a modeling point of view. In particular, it is shown that the proposed distribution accommodates various levels of skewness and kurtosis. Then, some statistical work is performed. We investigate diverse estimation methods for the involved parameters and evaluate their performance through two simulation studies. Subsequently, the quantile regression model derived from the proposed distribution is developed. Two real-world data applications of interest are provided. The first application is about the univariate modeling of the percentage of the educational attainment of some countries, which is one indicator of the education topic of the Better Life Index (BLI) of the Organization for Economic Co-operation and Development (OECD) countries. The second application is to explain the relationship between the percentage of educational attainment of some countries with one indicator of the work-life balance, safety, and health topics of BLI via median quantile regression modeling. For the considered data sets, the proposed distribution and quantile regression models show that they have better modeling abilities than competitive models under some comparison criteria. The results also indicate that covariates are (statistically) significant at any ordinary level of significance for the median response.
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Korkmaz, Mustafa Ç., Christophe Chesneau, and Zehra Sedef Korkmaz. "The Unit Folded Normal Distribution: A New Unit Probability Distribution with the Estimation Procedures, Quantile Regression Modeling and Educational Attainment Applications." Journal of Reliability and Statistical Studies, April 26, 2022. http://dx.doi.org/10.13052/jrss0974-8024.15111.
Full textAbstract:
In this paper, we develop a continuous distribution on the unit interval characterized by the distribution of the absolute hyperbolic tangent transformation of a random variable following the normal distribution. The lack of research on the prospect of hyperbolic transformations providing flexible distributions on the unit interval is a motivation for the study. First, we study it theoretically and discuss its properties of interest from a modeling point of view. In particular, it is shown that the proposed distribution accommodates various levels of skewness and kurtosis. Then, some statistical work is performed. We investigate diverse estimation methods for the involved parameters and evaluate their performance through two simulation studies. Subsequently, the quantile regression model derived from the proposed distribution is developed. Two real-world data applications of interest are provided. The first application is about the univariate modeling of the percentage of the educational attainment of some countries, which is one indicator of the education topic of the Better Life Index (BLI) of the Organization for Economic Co-operation and Development (OECD) countries. The second application is to explain the relationship between the percentage of educational attainment of some countries with one indicator of the work-life balance, safety, and health topics of BLI via median quantile regression modeling. For the considered data sets, the proposed distribution and quantile regression models show that they have better modeling abilities than competitive models under some comparison criteria. The results also indicate that covariates are (statistically) significant at any ordinary level of significance for the median response.
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Cyril, Umeh, Okwy, Nzotta, S. M., and Chris-Ejiogu, Uzoamaka Gloria. "Effect of Fiscal Policy on Poverty in Nigeria." Account and Financial Management Journal 08, no. 02 (February 28, 2023). http://dx.doi.org/10.47191/afmj/v8i2.05.
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The study investigated the effect of fiscal policy on poverty in Nigeria. The study covered 35-year period, spanning from 1986 to 2020 being a liberalized era in Nigerian economy. Fiscal policy being the explanatory variables, was disaggregated into federal government retained revenue (FRR), government capital expenditure (GCE), government recurrent expenditure (GRE),non-oil revenue (NOR), and public debt (PD). Poverty index as a dependent variable, being the unit measure for change in the poverty rate was used as proxy for poverty. Diagnostics test employed were, descriptive statistics to measure the mean, standard deviation, kurtosis and skewness as well as the Jarque-Bera statistics of the variables, while Augmented Dickey-Fuller unit root test was used to test for the stationarity of the data and Autoregressive distributive lag co-integration was employed to test for long-run relationship existing among the variables. Autoregressive distributive lag (ARDL) was used for the analysis since there was a long run relationship existing among the variables and Granger causality test was also employed to measure the directional relationship of the variables under study. The unit root test result showed that all the variables studied were stationary at first difference except NOR and FRR which were stationary at level, and co-integration result showed that fiscal policy has a significant long run relationship with poverty in Nigeria. The ARDL result showed that non-oil revenue showed initial negative government retained revenue but started effect of -3.298345 Lag 3, and then consistent positive effects 6.062662 Lag 4 through the short run periods. The federal government retained revenue showed initial negative effect of -3.739652 Lag 2followed by positive effect of 0.390469 Lag 3 and a return on positive effect of 6.249618 Lag 4 within the short run period. The government capital expenditure had similar trend as the federal retained revenue with a negative effect of -6.786381 in the initial period and first lag and then swung between positive and negative effects within the short run period, while the government recurrent expenditure started out with three year lagged period positive effects of 3.069591, 5.9766088, & 4.406814 but ended with negative effect of -7.978000. It was only the public debt profile that out rightly showed negative effect. Public debt has initial negative effect of -1.323569 and positive effect of 3.415523 Lag 3on the short run. Granger causality showed that non-oil revenue and public debt had causal effects on poverty reduction while federal government retained revenue, capital expenditure and recurrent expenditure do not have causal relationship with poverty reduction in Nigeria. The study concludes that fiscal policy with adjusted R2 of 0.993059 (99%) and p. value of 0.006425 has a significant effect on poverty in Nigeria. It was recommended that revenue and expenditure be increased while public debt be minimized in order to reduce poverty in Nigeria.
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Ezebunanwa, A. C., J. I. Eronin, V. Okorie, E. C. Mbagwu, and Njoku Achu Uchenna. "Facies Analysis of Eocene Sediment of Umuahia Area, Southeastern Nigeria." Journal of Geography, Environment and Earth Science International, October 23, 2019, 1–22. http://dx.doi.org/10.9734/jgeesi/2019/v23i430174.
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This research work is the detailed facies analysis of the depositional environments and paleogeographic setting of the Eocene sedimentary sequence (Ameki Formations) exposed in the Umuahia area and paleoclimate during that periods. The study area was mainly concentrated around Amaudara inUmuahia South and Ekeoha in Umuahia North. And the co-ordinate are as follows,location-1 0.5°30.80N, 0.7°26.93E, location-2 0.5°30.39N, 0.7°26.62E, location-3 0.5° 32.83N, 0.7°27.24 E and location-4 0.5°32.19 N, 0.7°26.13 E.
The aim of the study is to analyze the detailed sedimentary facies and describe the depositional environment in other to predict the depositional environment of the Eocene sediment (Ameki Formation) of the study area, which is underlain by rock unit of Ameki and predominately contains Laterite, mudstone, siltstone, claystone, sandstone and shale and Burrows were identified.
The rock sequence consist of reddish lateritic material, highly weathered mudstone capped with ripped bedded kaolinite clay unit, light grey claystone, cross-bedded sandstone with claystone, whitish sandstone, siltystone, fine-medium grained sandstone with pockets of mudclast capped with ferruginized ground and dark grey shale.
On the basis of gross lithology, sand-silt-clay percentage, color, texture and assemblage of sedimentary structure, eight distinct lithofacies type were recognized, grey shale facie (Gs), clay stone facie (Cs), cross-bedded sandstone facie (Cbs), mudstone facie (Mf), lateritic facie (Lf), mudstone facie (Bms), ferruginized sandstone facie (Fsf), sandstone facie (Bsf) are recognized within the lithosuccesion.
From the analysis, the facies are grouped into two facie association on the basis of grain size. The Fine-grained facies association (FFA) which consist of Gs, Cbs, Cs, Mf and Fst and the Medium to Fine-grained facies association (MFA) which also consist of Bms, Bsf and Lf. It also shows medium grained sand, moderately sorted to well sorted sandstone, Skewness ranged from symmetrical to positive skewed and kurtosis showed leptokurtic.
Deduction from facies analysis and grain size analysis shows that Ameki Formation consist of foraminifera and Mollusca which indicate that Ameki Formation was deposited in the estuarine(Marine) environment.
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Mahalingam, A., and N. Manivannan. "Interspecific Hybridization Between Vigna radiata and Vigna mungo Towards the Broadening of Genetic Base for MYMV Disease Resistance and Generating Variability." LEGUME RESEARCH - AN INTERNATIONAL JOURNAL, Of (April 10, 2021). http://dx.doi.org/10.18805/lr-4495.
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Background: The main cause for low yield in greengram is its susceptibility to Mungbean Yellow Mosaic Virus (MYMV) which is the most prevalent and destructive viral pathogen cause 85% yield lose. Inter specific hybridization between Vigna radiata and Vigna mungo could be an alternate approach to develop MYMV resistant genotypes in greengram which leads to additional source of creating variability for desirable attributes including yield, nutritional quality, biotic and abiotic stresses.Methods: The present investigation was carried out at National Pulses Research Centre (NPRC), Tamil Nadu Agricultural University, Vamban during 2016-2017. Interspecific hybridization has been effected between Vigna radiata var. VBN (Gg)2, VBN (Gg) 3 (as females) and Vigna mungo Var. Mash 114 (as male) during Summer 2016. The interspecific F1 hybrids, F2 and F3 populations were evaluated during Kharif 2016, Rabi 2016-17 and summer 2017 respectively. The F2 and F3 populations were evaluated for days to 1st flowering, days to maturity, plant height (cm), number of branches / plant, number of clusters / plant, number of pods / clusters, pod length (cm), number of seeds / pod, number of pods / plant, single plant yield (g.). The MYMV resistance has been confirmed under infector row method using CO 5 as susceptible check variety. Phenotypic coefficient of variation (PCV) and genotypic coefficient of variation (GCV), Heritability (h2) in broad sense, genetic advance as per cent of mean, Skewness and Kurtosis were estimated for yield and yield components. Result: Four true F1 plants were recovered in Vigna radiata var. VBN (Gg)2 x Vigna mungo Var. Mash 114 cross combination. The fertile F1 had the shallow lobbed leaf of Vigna radiata var. VBN (Gg)2 and black colour seed of Vigna mungo var. Mash 114 with pollen fertility of 42.0 per cent and crossability of 12.50%. Most interestingly all the four interspecific F1 plants were free from MYMV disease. In F2 generation, only one healthy plant was survived which had a pod, stem and branching behaviour of blackgram and greengram characters of lobbed leaf and green seed colour. In F3 generation, number of branches per plant, number of clusters per plant, number of pods per plant, pod length and seed yield per plant had high GCV, high PCV, high heritability coupled with high genetic advance as per cent of mean. Present study suggests that MYMV resistant cultivars of greengram can be explored through interspecific hybridization with Vigna mungo var. Mash 114 as a source of resistance and the hidden transgressive segregants can be recovered in F3 generation.
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Cui, Jinxin, and Aktham Maghyereh. "Time–frequency co-movement and risk connectedness among cryptocurrencies: new evidence from the higher-order moments before and during the COVID-19 pandemic." Financial Innovation 8, no. 1 (September 30, 2022). http://dx.doi.org/10.1186/s40854-022-00395-w.
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AbstractAnalyzing comovements and connectedness is critical for providing significant implications for crypto-portfolio risk management. However, most existing research focuses on the lower-order moment nexus (i.e. the return and volatility interactions). For the first time, this study investigates the higher-order moment comovements and risk connectedness among cryptocurrencies before and during the COVID-19 pandemic in both the time and frequency domains. We combine the realized moment measures and wavelet coherence, and the newly proposed time-varying parameter vector autoregression-based frequency connectedness approach (Chatziantoniou et al. in Integration and risk transmission in the market for crude oil a time-varying parameter frequency connectedness approach. Technical report, University of Pretoria, Department of Economics, 2021) using intraday high-frequency data. The empirical results demonstrate that the comovement of realized volatility between BTC and other cryptocurrencies is stronger than that of the realized skewness, realized kurtosis, and signed jump variation. The comovements among cryptocurrencies are both time-dependent and frequency-dependent. Besides the volatility spillovers, the risk spillovers of high-order moments and jumps are also significant, although their magnitudes vary with moments, making them moment-dependent as well and are lower than volatility connectedness. Frequency connectedness demonstrates that the risk connectedness is mainly transmitted in the short term (1–7 days). Furthermore, the total dynamic connectedness of all realized moments is time-varying and has been significantly affected by the outbreak of the COVID-19 pandemic. Several practical implications are drawn for crypto investors, portfolio managers, regulators, and policymakers in optimizing their investment and risk management tactics.