Journal articles on the topic 'Multivariate location models'
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Ahmed, S. E., and A. K. Md. E.Saleh. "Improved nonparametric estimation of location vectors in multivariate regression models." Journal of Nonparametric Statistics 11, no. 1-3 (January 1999): 51–78. http://dx.doi.org/10.1080/10485259908832775.
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McCluskey, William J., and Richard A. Borst. "Specifying the effect of location in multivariate valuation models for residential properties." Property Management 25, no. 4 (August 21, 2007): 312–43. http://dx.doi.org/10.1108/02637470710775185.
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Arnold, Barry C., and Robert J. Beaver. "Alternative constructions of skewed multivariate distributions." Acta et Commentationes Universitatis Tartuensis de Mathematica 8 (December 31, 2004): 73–81. http://dx.doi.org/10.12697/acutm.2004.08.03.
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A review of the construction of skewed multivariate normal distributions is presented. The review considers construction via (1) hidden truncation, (2) threshold models, (3) additive components and (4) a location and scale change for k variables beginning with k−1 independent standard normal variates and one univariate skew normal density. Extensiom to non-normal distributions have mainly used the hidden truncation approach. Unlike the normal case, the use of the three remaining techniques in constructing non-normal multivariate distributions leads to models distinct from those found using the hidden truncation approach. Examples of several tractable multivariate distributions using methods (1) and (3) are also presented.
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Akpootu, D. O., M. I. Iliyasu, B. M. Olomiyesan, S. A. Fagbemi, S. B. Sharafa, M. Idris, Z. Abdullahi, and N. O. Meseke. "MULTIVARIATE MODELS FOR PREDICTING GLOBAL SOLAR RADIATION IN JOS, NIGERIA." Matrix Science Mathematic 6, no. 1 (2022): 05–12. http://dx.doi.org/10.26480/msmk.01.2022.05.12.
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This study developed two to six multivariate regression equations that reliably predict global radiation in Jos (Latitude 9.87 °𝑁 and Longitude 8.75 °𝐸). Thirty-one years (1980 – 2010) observed monthly mean daily global solar radiation, sunshine hours, maximum and minimum temperatures, cloud cover, rainfall, relative humidity and wind speed data were used in this study with the clearness index as the response variable and other variables as predictors. The seven validation indices employed are the coefficient of determination (R2), Mean Bias Error (MBE), Root Mean Square Error (RMSE), Mean Percentage Error (MPE), t – test, Nash – Sutcliffe Equation (NSE) and Index of Agreement (IA) to determine the reliability, suitability and applicability of the developed models. The results in this study revealed that all the developed multivariate models were found reliable for global solar radiation estimation in Jos depending on the obtainable meteorological data measured in the location. The correlation between the measured and predicted (developed) global solar radiation shows a perfect correlation as depicted from the figures.
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Chandra, Srinivasa Ravi, and Haitham Al-Deek. "Cross-Correlation Analysis and Multivariate Prediction of Spatial Time Series of Freeway Traffic Speeds." Transportation Research Record: Journal of the Transportation Research Board 2061, no. 1 (January 2008): 64–76. http://dx.doi.org/10.3141/2061-08.
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Short-term traffic prediction on freeways is one of the critical components of the advanced traveler information system (ATIS). The traditional methods of prediction have used univariate ARIMA time-series models based on the autocorrelation function of the time series of traffic variables at a location. However, the effect of upstream and downstream location information has been largely neglected or underused in the case of freeway traffic prediction. The purpose of this study is to demonstrate the effect of upstream as well as downstream locations on the traffic at a specific location. To achieve this goal, a section of five stations extending over 2.5 mi on I-4 in the downtown region of Orlando, Florida, was selected. The speeds from a station at the center of this location were then checked for cross-correlations with stations upstream and downstream. The cross-correlation function is analogous to the autocorrelation function extended to two variables. It indicates whether the past values of an input series influence the future values of a response series. It was found in this study that the past values of upstream as well as downstream stations influence the future values at a station and therefore can be used for prediction. A vector autoregressive model was found appropriate and better than the traditional ARIMA model for prediction at these stations.
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Cheng, I., X. Xu, and L. Zhang. "Overview of receptor-based source apportionment studies for speciated atmospheric mercury." Atmospheric Chemistry and Physics 15, no. 14 (July 17, 2015): 7877–95. http://dx.doi.org/10.5194/acp-15-7877-2015.
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Abstract. Receptor-based source apportionment studies of speciated atmospheric mercury are not only concerned with source contributions but also with the influence of transport, transformation, and deposition processes on speciated atmospheric mercury concentrations at receptor locations. Previous studies applied multivariate receptor models including principal components analysis and positive matrix factorization, and back trajectory receptor models including potential source contribution function, gridded frequency distributions, and concentration–back trajectory models. Combustion sources (e.g., coal combustion, biomass burning, and vehicular, industrial and waste incineration emissions), crustal/soil dust, and chemical and physical processes, such as gaseous elemental mercury (GEM) oxidation reactions, boundary layer mixing, and GEM flux from surfaces were inferred from the multivariate studies, which were predominantly conducted at receptor sites in Canada and the US. Back trajectory receptor models revealed potential impacts of large industrial areas such as the Ohio River valley in the US and throughout China, metal smelters, mercury evasion from the ocean and the Great Lakes, and free troposphere transport on receptor measurements. Input data and model parameters specific to atmospheric mercury receptor models are summarized and model strengths and weaknesses are also discussed. Multivariate models are suitable for receptor locations with intensive air monitoring because they require long-term collocated and simultaneous measurements of speciated atmospheric Hg and ancillary pollutants. The multivariate models provide more insight about the types of Hg emission sources and Hg processes that could affect speciated atmospheric Hg at a receptor location, whereas back trajectory receptor models are mainly ideal for identifying potential regional Hg source locations impacting elevated Hg concentrations. Interpretation of the multivariate model output to sources can be subjective and challenging when speciated atmospheric Hg is not correlated with ancillary pollutants and when source emissions profiles and knowledge of Hg chemistry are incomplete. The majority of back trajectory receptor models have not accounted for Hg transformation and deposition processes and could not distinguish between upwind and downwind sources effectively. Ensemble trajectories should be generated to take into account the trajectory uncertainties where possible. One area of improvement that applies to all the receptor models reviewed in this study is the greater focus on evaluating the accuracy of the models at identifying potential speciated atmospheric mercury sources, source locations, and chemical and physical processes in the atmosphere. In addition to receptor model improvements, the data quality of speciated atmospheric Hg plays an equally important part in producing accurate receptor model results.
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Cranshaw, Justin, Jonathan Mugan, and Norman Sadeh. "User-Controllable Learning of Location Privacy Policies With Gaussian Mixture Models." Proceedings of the AAAI Conference on Artificial Intelligence 25, no. 1 (August 4, 2011): 1146–52. http://dx.doi.org/10.1609/aaai.v25i1.8097.
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With smart-phones becoming increasingly commonplace, there has been a subsequent surge in applications that continuously track the location of users. However, serious privacy concerns arise as people start to widely adopt these applications. Users will need to maintain policies to determine under which circumstances to share their location. Specifying these policies however, is a cumbersome task, suggesting that machine learning might be helpful. In this paper, we present a user-controllable method for learning location sharing policies. We use a classifier based on multivariate Gaussian mixtures that is suitably modified so as to restrict the evolution of the underlying policy to favor incremental and therefore human-understandable changes as new data arrives. We evaluate the model on real location-sharing policies collected from a live location-sharing social network, and we show that our method can learn policies in a user-controllable setting that are just as accurate as policies that do not evolve incrementally. Additionally, we highlight the strength of the generative modeling approach we take, by showing how our model easily extends to the semi-supervised setting.
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Teräsvirta, Timo. "Mathematical and Quantitative Methods: Dynamic Models for Volatility and Heavy Tails: With Applications to Financial and Economic Time Series." Journal of Economic Literature 51, no. 4 (December 1, 2013): 1190–92. http://dx.doi.org/10.1257/jel.51.4.1183.r4.
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Timo Terasvirta of Aarhus University reviews, “Dynamic Models for Volatility and Heavy Tails: With Applications to Financial and Economic Time Series” by Andrew C. Harvey. The Econlit abstract of this book begins: “Presents a theory for a class of nonlinear time series models that can deal with dynamic distributions, with an emphasis on models in which the conditional distribution of an observation may be heavy-tailed and the location and/or scale changes over time. Discusses statistical distributions and asymptotic theory; location; scale; location/scale models for nonnegative variables; dynamic kernel density estimation and time-varying quantiles; multivariate models, correlation, and association; and further directions in dynamic models. Harvey is Professor of Econometrics at the University of Cambridge and Fellow of Corpus Christi College, the Econometric Society, and the British Academy.”
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Yates, A. R., and K. A. Yeomans. "The selection of food shopping locations by UK suburban households: A multivariate analysis." South African Journal of Business Management 16, no. 4 (December 31, 1985): 171–80. http://dx.doi.org/10.4102/sajbm.v16i4.1092.
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In this article a behavioural model of retail location is discussed which differs from established models in two respects. Firstly, in the behavioural model the demand for retail outlets is emphasized rather than the supply of retail outlets. Secondly, psychological and sociological constructs are used to help explain store selection behaviour. The behavioural model differs from established location theory in that an attempt is made to study the act of shopping in relation to other human activities. The basic postulate of the model is that people are constrained in their shopping behaviour. People can then be rated on a scale of constraints, which can be used to predict store selection behaviour.
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Chandrasekar, B., and S. Amala Revathy. "Equivariant estimation for the parameters of location-scale multivariate exponential models and its application in reliability analysis." Communications in Statistics - Theory and Methods 45, no. 18 (December 17, 2015): 5550–59. http://dx.doi.org/10.1080/03610926.2014.948195.
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Pal, Nabendu, and Ching-Hui Chano. "Risk Analysis and Robustness of four Shrinkage Estimators." Calcutta Statistical Association Bulletin 46, no. 1-2 (March 1996): 35–62. http://dx.doi.org/10.1177/0008068319960105.
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In this paper we first investigate the risk properties of four shrinkage estimators of a location vector assuming that the random vector follows a multivariate normal distribution. Next we study the risk performance of these four estimators under a multivariate 1 distribution with n(⩾ 3) degrees of freedom. It bas been found that under both the models the four shrinkage estimators preserve almost the same relative merits and demerits. AMS 1991 Subject Classifications : Primary 62C15, Secondary 62H12.
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Cook, Jared A., Ralph C. Smith, Jason M. Hite, Razvan Stefanescu, and John Mattingly. "Application and Evaluation of Surrogate Models for Radiation Source Search." Algorithms 12, no. 12 (December 12, 2019): 269. http://dx.doi.org/10.3390/a12120269.
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Surrogate models are increasingly required for applications in which first-principles simulation models are prohibitively expensive to employ for uncertainty analysis, design, or control. They can also be used to approximate models whose discontinuous derivatives preclude the use of gradient-based optimization or data assimilation algorithms. We consider the problem of inferring the 2D location and intensity of a radiation source in an urban environment using a ray-tracing model based on Boltzmann transport theory. Whereas the code implementing this model is relatively efficient, extension to 3D Monte Carlo transport simulations precludes subsequent Bayesian inference to infer source locations, which typically requires thousands to millions of simulations. Additionally, the resulting likelihood exhibits discontinuous derivatives due to the presence of buildings. To address these issues, we discuss the construction of surrogate models for optimization, Bayesian inference, and uncertainty propagation. Specifically, we consider surrogate models based on Legendre polynomials, multivariate adaptive regression splines, radial basis functions, Gaussian processes, and neural networks. We detail strategies for computing training points and discuss the merits and deficits of each method.
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Kapur, Kush, Xue Li, Emily A. Blood, and Donald Hedeker. "Bayesian mixed-effects location and scale models for multivariate longitudinal outcomes: an application to ecological momentary assessment data." Statistics in Medicine 34, no. 4 (November 20, 2014): 630–51. http://dx.doi.org/10.1002/sim.6345.
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Zhao, Lei, J. Matthijs Biesbroek, Lin Shi, Wenyan Liu, Hugo J. Kuijf, Winnie WC Chu, Jill M. Abrigo, et al. "Strategic infarct location for post-stroke cognitive impairment: A multivariate lesion-symptom mapping study." Journal of Cerebral Blood Flow & Metabolism 38, no. 8 (September 12, 2017): 1299–311. http://dx.doi.org/10.1177/0271678x17728162.
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Lesion location is an important determinant for post-stroke cognitive impairment. Although several ‘strategic’ brain regions have previously been identified, a comprehensive map of strategic brain regions for post-stroke cognitive impairment is lacking due to limitations in sample size and methodology. We aimed to determine strategic brain regions for post-stroke cognitive impairment by applying multivariate lesion-symptom mapping in a large cohort of 410 acute ischemic stroke patients. Montreal Cognitive Assessment at three to six months after stroke was used to assess global cognitive functioning and cognitive domains (memory, language, attention, executive and visuospatial function). The relation between infarct location and cognition was assessed in multivariate analyses at the voxel-level and the level of regions of interest using support vector regression. These two assumption-free analyses consistently identified the left angular gyrus, left basal ganglia structures and the white matter around the left basal ganglia as strategic structures for global cognitive impairment after stroke. A strategic network involving several overlapping and domain-specific cortical and subcortical structures was identified for each of the cognitive domains. Future studies should aim to develop even more comprehensive infarct location-based models for post-stroke cognitive impairment through multicenter studies including thousands of patients.
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Dannemann Dugick, Fransiska K., Philip S. Blom, Brian W. Stump, Chris T. Hayward, Stephen J. Arrowsmith, Joshua C. Carmichael, and Omar E. Marcillo. "Evaluating the location capabilities of a regional infrasonic network in Utah, US, using both ray tracing-derived and empirical-derived celerity-range and backazimuth models." Geophysical Journal International 229, no. 3 (March 23, 2022): 2133–46. http://dx.doi.org/10.1093/gji/ggac027.
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SUMMARY More realistic models for infrasound signal propagation across a region can be used to improve the precision and accuracy of spatial and temporal source localization estimates. Motivated by incomplete infrasound event bulletins in the Western US, the location capabilities of a regional infrasonic network of stations located between 84–458 km from the Utah Test and Training Range, Utah, USA, is assessed using a series of near-surface explosive events with complementary ground truth (GT) information. Signal arrival times and backazimuth estimates are determined with an automatic F-statistic based signal detector and manually refined by an analyst. This study represents the first application of three distinct celerity-range and backazimuth models to an extensive suite of realistic signal detections for event location purposes. A singular celerity and backazimuth deviation model was previously constructed using ray tracing analysis based on an extensive archive of historical atmospheric specifications and is applied within this study to test location capabilities. Similarly, a set of multivariate, season and location specific models for celerity and backazimuth are compared to an empirical model that depends on the observations across the infrasound network and the GT events, which accounts for atmospheric propagation variations from source to receiver. Discrepancies between observed and predicted signal celerities result in locations with poor accuracy. Application of the empirical model improves both spatial localization precision and accuracy; all but one location estimates retain the true GT location within the 90 per cent confidence bounds. Average mislocation of the events is 15.49 km and average 90 per cent error ellipse areas are 4141 km2. The empirical model additionally reduces origin time residuals; origin time residuals from the other location models are in excess of 160 s while residuals produced with the empirical model are within 30 s of the true origin time. We demonstrate that event location accuracy is driven by a combination of signal propagation model and the azimuthal gap of detecting stations. A direct relationship between mislocation, error ellipse area and increased station azimuthal gaps indicate that for sparse networks, detection backazimuths may drive location biases over traveltime estimates.
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Nagaraj, Meghana, and Roshan Srivastav. "Spatial multivariate selection of climate indices for precipitation over India." Environmental Research Letters 17, no. 9 (August 26, 2022): 094014. http://dx.doi.org/10.1088/1748-9326/ac8a06.
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Abstract Large-scale interdependent teleconnections influence precipitation at various spatio-temporal scales. Selecting the relevant climate indices based on geographical location is important. Therefore, this study focuses on the spatial multivariate selection of climate indices influencing precipitation variability over India, using the partial least square regression and variable importance of projection technique. 17 climate indices and gridded precipitation dataset (0.25 × 0.25°) from the Indian Meteorological Department for 1951–2020 at a monthly scale are considered. Results show that among all the indices, Nino 4, Nino 1 + 2, Trans Nino Index, Atlantic Multidecadal Oscillation (AMO), quasi-biennial oscillation (QBO), Arctic oscillation (AO), and North Atlantic Oscillation (NAO) have a significant influence on precipitation over India. Further, within homogenous regions, it is found that the Southern Oscillation Index and Nino 3.4 are selected majorly in the South Peninsular compared to other regions. The NAO/AO show a similar pattern and was found to be relevant in the Northeast region (>89%). AMO is selected mainly in Northwest, and West Central (>80%), AMO and QBO at about 70% of grid locations over Central Northeast India. It is to be noted that the number of climate indices identified varies spatially across the study region. Overall, the study highlights identifying the relevant climate indices would aid in developing improved predictive and parsimonious models for agriculture planning and water resources management
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Cai, Ying, Andrew D. Sheldon, Qing Yu, and Bradley R. Postle. "Overlapping and distinct contributions of stimulus location and of spatial context to nonspatial visual short-term memory." Journal of Neurophysiology 121, no. 4 (April 1, 2019): 1222–31. http://dx.doi.org/10.1152/jn.00062.2019.
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Stimulus location is not always informative during visual short-term memory (VSTM) for nonspatial features. Nevertheless, there is considerable evidence for the automatic encoding and retention of location information, regardless of its task relevance. To explore the functional and neural bases of the representation of spatial context in VSTM for nonspatial information, functional magnetic resonance imaging was performed while subjects performed delayed recall for the orientation of individual stimuli. Stimulus location varied across trials, and although this information was irrelevant for task performance, multivariate pattern analysis decoding of stimulus location sustained across trials, and also the decoding strength, predicted the precision of the recall of orientation. The influence of spatial context on the representation of orientation was operationalized by comparing the orientation reconstructions with multivariate inverted encoding models (IEM) trained in location context-dependent vs. -independent data. Although orientation reconstructions were robust for both location-dependent and location-independent IEMs, they were markedly stronger for the former. Furthermore, the functional relevance of location context was demonstrated by the fact that only the location-dependent neural representations of stimulus orientation predicted recall precision. NEW & NOTEWORTHY Neural representation strength of stimulus location predicts the precision of visual short-term memory (VSTM) recall of nonspatial stimulus, even when this information is task irrelevant. Neural representations of nonspatial stimuli that incorporate location context are stronger than those that do not, and only the former representations are strongly linked to behavior. The contributions to nonspatial VSTM performance of the representation of location context are at least partly distinct from those of the representation of stimulus content.
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Archontakis, Fragiskos, and Rocco Mosconi. "Søren Johansen and Katarina Juselius: A Bibliometric Analysis of Citations through Multivariate Bass Models." Econometrics 9, no. 3 (August 12, 2021): 30. http://dx.doi.org/10.3390/econometrics9030030.
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We showcase the impact of Katarina Juselius and Søren Johansen’s contribution to econometrics using bibliometric data on citations from 1989 to 2017, extracted from the Web of Science (WoS) database. Our purpose is to analyze the impact of KJ and SJ’s ideas on applied and methodological research in econometrics. To this aim, starting from WoS data, we derived two composite indices whose purpose is to disentangle the authors’ impact on applied research from their impact on methodological research. As of 2017, the number of applied citing papers per quarter had not yet reached the peak; conversely, the peak in the methodological literature seem to have been reached around 2000, although the shape of the trajectory is very flat after the peak. We analyzed the data using a multivariate dynamic version of the well known Bass model. Our estimates suggest that the methodological literature is mainly driven by “innovators”, whereas “imitators” are relatively more important in the applied literature: this might explain the different location of the peaks. We also find that, in the literature referring to KJ and SJ, the “cross-fertilization” between methodological and applied research is statistically significant and bi-directional.
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Gad, Mahmoud, Sayeda M. Abdo, Anyi Hu, Mohamed Azab El-Liethy, Mohamed S. Hellal, Hala S. Doma, and Gamila H. Ali. "Performance Assessment of Natural Wastewater Treatment Plants by Multivariate Statistical Models: A Case Study." Sustainability 14, no. 13 (June 23, 2022): 7658. http://dx.doi.org/10.3390/su14137658.
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Waste stabilization ponds (WSPs) as natural wastewater treatment plants are commonly utilized for wastewater treatment due to their simple design, low cost, and low-skilled operator requirements. Large-scale studies assessing the performance of WSPs using multivariate statistical models are scarce. Therefore, this study was conducted to assess the performance of 16 full-scale WSPs regarding physicochemical parameters, algae, bacterial indicators, and pathogens (e.g., Cryptosporidium, Entamoeba histolytica) by using multivariate statistical models. The principal component analysis revealed that the chemical pollutants were removed significantly (p < 0.001) through the treatment stages of 16 WSPs, indicating that the treatment stages made a substantial change in the environmental parameters. The non-multidimensional scale analysis revealed that the treatment stages restructured the bacterial indicators significantly (p < 0.001) in the WSPs, implying that the bacterial indicators were removed with the progress of the treatment processes. The algal community exhibited a distinct pattern between the geographical location (i.e., upper WSPs versus lower WSPs) and different treatment stages (p < 0.001). Four out of the sixteen WSPs did not comply with the Egyptian ministerial decree 48/1982 for discharge in agriculture drainage; three of these stations are in lower Egypt (M.K., Al-Adlia, and Ezbet El-Borg), and one is in upper Egypt (Armant). The continuous monitoring of WSPs for compliance with regulatory guidelines with the aid of multivariate statistical models should be routinely performed.
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O. Akpootu, Davidson, Bello I. Tijjani, and Usman M. Gana. "Empirical models for predicting global solar radiation using meteorological parameters for Sokoto, Nigeria." International Journal of Physical Research 7, no. 2 (July 22, 2019): 48. http://dx.doi.org/10.14419/ijpr.v7i2.29160.
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The performances of sunshine, temperature and multivariate models for the estimation of global solar radiation for Sokoto (Latitude 13.020N, Longitude 05.250E and 350.8 m asl) located in the Sahelian region in Nigeria were evaluated using measured monthly average daily global solar radiation, maximum and minimum temperatures, sunshine hours, rainfall, wind speed, cloud cover and relative humidity meteorological data during the period of thirty one years (1980-2010). The comparison assessment of the models was carried out using statistical indices of coefficient of determination (R2), Mean Bias Error (MBE), Root Mean Square Error (RMSE), Mean Percentage Error (MPE), t – test, Nash – Sutcliffe Equation (NSE) and Index of Agreement (IA). For the sunshine based models, a total of ten (10) models were developed, nine (9) existing and one author’s sunshine based model. For the temperature based models, a total of four (4) models were developed, three (3) existing and one author’s temperature based model. The results of the existing and newly developed author’s sunshine and temperature based models were compared and the best empirical model was identified and recommended. The results indicated that the author’s quadratic sunshine based model involving the latitude and the exponent temperature based models are found more suitable for global solar radiation estimation in Sokoto. The evaluated existing Ångström type sunshine based model for the location was compared with those available in literature from other studies and was found more suitable for estimating global solar radiation. Comparing the most suitable sunshine and temperature based models revealed that the temperature based models is more appropriate in the location. The developed multivariate regression models are found suitable as evaluation depends on the available combination of the meteorological parameters based on two to six variable correlations. The recommended models are found suitable for estimating global solar radiation in Sokoto and regions with similar climatic information with higher accuracy and climatic variability.
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Mardalena, Selvi, Purhadi Purhadi, Jerry Dwi Trijoyo Purnomo, and Dedy Dwi Prastyo. "The Geographically Weighted Multivariate Poisson Inverse Gaussian Regression Model and Its Applications." Applied Sciences 12, no. 9 (April 21, 2022): 4199. http://dx.doi.org/10.3390/app12094199.
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This study aims to develop a method for multivariate spatial overdispersion count data with mixed Poisson distribution, namely the Geographically Weighted Multivariate Poisson Inverse Gaussian Regression (GWMPIGR) model. The parameters of the GWMPIGR model are estimated locally using the maximum likelihood estimation (MLE) method by considering spatial effects. Therefore, the significance of the regression parameter differs for each location. In this study, four GWMPIGR models are evaluated based on the exposure variable and the spatial weighting function. We compare the performance of those four models in real-world application using data on the number of infant, under-5 and maternal deaths in East Java in 2019 using five predictor variables. In this study, the GWMPIGR model uses one exposure variable and three exposure variables. Compared to the fixed kernel Gaussian weighting function, the GWMPIGR model with the fixed kernel bisquare weighting function and one exposure variable has a better fit based on the AICc value. Furthermore, according to the best GWMPIGR model, there are several regional groups formed based on predictors that significantly affected each event in East Java in 2019.
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Li, Cheng, Jia Liu, Jiamao Lin, Zhenxiang Li, Xiaoling Shang, and Haiyong Wang. "Poor survival of non-small-cell lung cancer patients with main bronchus tumor: a large population-based study." Future Oncology 15, no. 24 (August 2019): 2819–27. http://dx.doi.org/10.2217/fon-2019-0098.
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Aim: In this study, we evaluated the association between tumor location and prognosis in non-small-cell lung cancer patients. Patients & methods: The SEER database was used to screen for suitable patients using our inclusion criteria. The χ2 test was used to compare baseline patient characteristics and the Kaplan–Meier method as well as the log-rank test were used to compare survival differences. At last, univariate and multivariate Cox proportional hazards regression models were used to analyze the influence of different variables on overall survival. Results: The results found no significant difference in overall survival between patients in laterality (p = 0.071). However, patients with main bronchial tumors had worse prognosis than tumors at other locations (p < 0.001). Our results also showed that tumor location including main bronchus, upper lobe, middle lobe, lower lobe and overlapping lesion was a significant factor affecting survival (p < 0.001). Subgroup analysis revealed that regardless of histology or M stage, patients with main bronchial tumors had a worse survival compared with other tumor locations (all; p < 0.001). Interestingly, we found that patients with tumor main bronchial tumors were more likely to be squamous carcinoma and terminal Tumor, Node, Metastasis stage (all; p < 0.001). Conclusion: Non-small-cell lung cancer patients’ prognosis was related to the tumor location. And patients with tumors located in main bronchus had worse outcomes than those located in other locations. Tumor primary site should be considered in treatment management and prognosis assessment.
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Varona, L., L. Gómez-Raya, W. M. Rauw, A. Clop, C. Ovilo, and J. L. Noguera. "Derivation of a Bayes Factor to Distinguish Between Linked or Pleiotropic Quantitative Trait Loci." Genetics 166, no. 2 (February 1, 2004): 1025–35. http://dx.doi.org/10.1093/genetics/166.2.1025.
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Abstract A simple procedure to calculate the Bayes factor between linked and pleiotropic QTL models is presented. The Bayes factor is calculated from the marginal prior and posterior densities of the locations of the QTL under a linkage and a pleiotropy model. The procedure is computed with a Gibbs sampler, and it can be easily applied to any model including the location of the QTL as a variable. The procedure was compared with a multivariate least-squares method. The proposed procedure showed better results in terms of power of detection of linkage when low information is available. As information increases, the performance of both procedures becomes similar. An example using data provided by an Iberian by Landrace pig intercross is presented. The results showed that three different QTL segregate in SSC6: a pleiotropic QTL affects myristic, palmitic, and eicosadienoic fatty acids; another pleiotropic QTL affects palmitoleic, stearic, and vaccenic fatty acids; and a third QTL affects the percentage of linoleic acid. In the example, the Bayes factor approach was more powerful than the multivariate least-squares approach.
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Kim, Jinho, and Mark Wilson. "Polytomous Item Explanatory Item Response Theory Models." Educational and Psychological Measurement 80, no. 4 (December 13, 2019): 726–55. http://dx.doi.org/10.1177/0013164419892667.
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This study investigates polytomous item explanatory item response theory models under the multivariate generalized linear mixed modeling framework, using the linear logistic test model approach. Building on the original ideas of the many-facet Rasch model and the linear partial credit model, a polytomous Rasch model is extended to the item location explanatory many-facet Rasch model and the step difficulty explanatory linear partial credit model. To demonstrate the practical differences between the two polytomous item explanatory approaches, two empirical studies examine how item properties explain and predict the overall item difficulties or the step difficulties each in the Carbon Cycle assessment data and in the Verbal Aggression data. The results suggest that the two polytomous item explanatory models are methodologically and practically different in terms of (a) the target difficulty parameters of polytomous items, which are explained by item properties; (b) the types of predictors for the item properties incorporated into the design matrix; and (c) the types of item property effects. The potentials and methodological advantages of item explanatory modeling are discussed as well.
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Hoffman, Lesa, and Ryan W. Walters. "Catching Up on Multilevel Modeling." Annual Review of Psychology 73, no. 1 (January 4, 2022): 659–89. http://dx.doi.org/10.1146/annurev-psych-020821-103525.
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This review focuses on the use of multilevel models in psychology and other social sciences. We target readers who are catching up on current best practices and sources of controversy in the specification of multilevel models. We first describe common use cases for clustered, longitudinal, and cross-classified designs, as well as their combinations. Using examples from both clustered and longitudinal designs, we then address issues of centering for observed predictor variables: its use in creating interpretable fixed and random effects of predictors, its relationship to endogeneity problems (correlations between predictors and model error terms), and its translation into multivariate multilevel models (using latent-centering within multilevel structural equation models). Finally, we describe novel extensions—mixed-effects location–scale models—designed for predicting differential amounts of variability.
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Klein Schaarsberg, F. L. H., A. C. de Niet, H. Zandberg, and Gerrit Jan Dijkgraaf. "Investigating the relationship between train speed and ground vibrations using random forest machine learning models." INTER-NOISE and NOISE-CON Congress and Conference Proceedings 263, no. 3 (August 1, 2021): 3595–606. http://dx.doi.org/10.3397/in-2021-2463.
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In the Netherlands, concerned citizens have proposed reducing train speed as an effective measure to mitigate annoyance caused by railway-induced vibrations. In the present study the relationship between train speed and other influencing parameters (e.g. axle load, wheel roughness), and ground vibrations was investigated using measurements, at different locations, of ground vibrations caused by the passage of regular freight trains and a test train at different speeds. Measurements have been analysed using multivariate regression models and a random decision forest model. The prevailing uncertainties have also been measured using normalized mean deviation between the model predicted value and the actual value. A comparison of results demonstrates that a 'trained and tested' random forest model has certain predictive advantages: i) mean deviation between predicted and actual value is found to be the lowest with random forest model; ii) the random forest model considers all available parameters in the dataset, thus simulating the real situation more closely. However, the model is very location-specific and must therefore be used with caution. In general it is observed that a decrease in train speed results in the reduction of measured vibration levels.
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Etherington, Thomas R. "Mahalanobis distances for ecological niche modelling and outlier detection: implications of sample size, error, and bias for selecting and parameterising a multivariate location and scatter method." PeerJ 9 (May 11, 2021): e11436. http://dx.doi.org/10.7717/peerj.11436.
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The Mahalanobis distance is a statistical technique that has been used in statistics and data science for data classification and outlier detection, and in ecology to quantify species-environment relationships in habitat and ecological niche models. Mahalanobis distances are based on the location and scatter of a multivariate normal distribution, and can measure how distant any point in space is from the centre of this kind of distribution. Three different methods for calculating the multivariate location and scatter are commonly used: the sample mean and variance-covariance, the minimum covariance determinant, and the minimum volume ellipsoid. The minimum covariance determinant and minimum volume ellipsoid were developed to be robust to outliers by minimising the multivariate location and scatter for a subset of the full sample, with the proportion of the full sample forming the subset being controlled by a user-defined parameter. This outlier robustness means the minimum covariance determinant and the minimum volume ellipsoid are highly relevant for ecological niche analyses, which are usually based on natural history observations that are likely to contain errors. However, natural history observations will also contain extreme bias, to which the minimum covariance determinant and the minimum volume ellipsoid will also be sensitive. To provide guidance for selecting and parameterising a multivariate location and scatter method, a series of virtual ecological niche modelling experiments were conducted to demonstrate the performance of each multivariate location and scatter method under different levels of sample size, errors, and bias. The results show that there is no optimal modelling approach, and that choices need to be made based on the individual data and question. The sample mean and variance-covariance method will perform best on very small sample sizes if the data are free of error and bias. At larger sample sizes the minimum covariance determinant and minimum volume ellipsoid methods perform as well or better, but only if they are appropriately parameterised. Modellers who are more concerned about the prevalence of errors should retain a smaller proportion of the full data set, while modellers more concerned about the prevalence of bias should retain a larger proportion of the full data set. I conclude that Mahalanobis distances are a useful niche modelling technique, but only for questions relating to the fundamental niche of a species where the assumption of multivariate normality is reasonable. Users of the minimum covariance determinant and minimum volume ellipsoid methods must also clearly report their parameterisations so that the results can be interpreted correctly.
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Gan, Mi, Dandan Li, Mingfei Wang, Guangyuan Zhang, Shuai Yang, and Jiyang Liu. "Optimal Urban Logistics Facility Location with Consideration of Truck-Related Greenhouse Gas Emissions: A Case Study of Shenzhen City." Mathematical Problems in Engineering 2018 (June 14, 2018): 1–14. http://dx.doi.org/10.1155/2018/8439582.
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The logistics facility location is always involved with great deals of investment. Its construction and operation also bring out a huge amount of the greenhouse gas (GHG) emission due to the consumption of building materials, energy, the running of trucks, and other logistics equipment. Particularly, trucking activities in the urban logistics networks (ULN) are a major source of GHG. This paper aims to formulate an eco-facility location model to minimize both the total cost of ULN construction and operation and the GHG emissions of truck trips. Based on the mathematical relations of GHG emissions rates and several macroscopic factors, which we obtained by multivariate regression analysis on a large set of empirical trucking data in our previous research, the data-driven emissions rates estimation function is acquired. Then, we link the estimation function of each trip purpose by various kinds of logistics facilities through a qualitative analysis. The eco-facility location problem is modeled by integrating the pure facility location model and the GHG emissions function. The problem is first converted to a biobjective mixed-integer program, and the Particle Swarm Optimization algorithm is applied to solve the model. Through experiments with real case, the effectiveness of the models and algorithms is verified. The eco-facility location model for ULN tends to obtain the environment-friendly location decision. Our analytical results also verify the hypothesis that locations of facility do impact the relevant truck-related GHG emissions, especially to transfer transport, as well as inbound and outbound freight.
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Miyoshi, Norikatsu, Masayuki Ohue, Shingo Noura, Masayoshi Yasui, Keijiro Sugimura, Akira Tomokuni, Hirofumi Akita, et al. "Prognostic Prediction Models for Colorectal Cancer Patients After Curative Resection." International Surgery 101, no. 9-10 (September 1, 2016): 406–13. http://dx.doi.org/10.9738/intsurg-d-15-00258.1.
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To develop a prediction tool for recurrence and survival in colorectal cancer (CRC) patients following surgically curative resections. We developed a reliable prediction model for CRC patients after surgically curative resections. Using clinicopathologic factors, novel prediction models were constructed with the area under the curve (AUC) of 0.841 and 0.876 for DFS and CSS, respectively. Between January 2004 and December 2007, 376 CRC patients were investigated at the Osaka Medical Center for Cancer and Cardiovascular Diseases. Patients with at least 1 of the following criteria were excluded: preoperative treatment, synchronous distant metastasis, noncurative resection, and incomplete follow-up after operation. All patients were retrospectively analyzed. A Cox proportional hazards model was used to develop a prediction model for disease-free survival (DFS) and cancer-specific survival (CSS). In univariate and multivariate analyses of clinicopathologic factors, the following factors had significant correlation with DFS and CSS: tumor location, preoperative serum carcinoembryonic antigen (CEA), pathologically defined tumor invasion, and lymph node metastasis. Using these variables, novel prediction models were constructed by the logistic regression model with AUC of 0.840 and 0.876 for DFS and CSS, respectively. The prediction models were validated by external datasets in an independent patient group. This study showed novel and reliable personalized prognostic models, integrating not only TNM factors but also tumor location and preoperative serum CEA to predict patient prognosis. These individualized prediction models could help clinicians in the treatment of postoperative CRC patients.
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Vinh-Hung, Vincent, and Richard Gordon. "Quantitative Target Sizes for Breast Tumor Detection Prior to Metastasis: A Prerequisite to Rational Design of 4D Scanners for Breast Screening." Technology in Cancer Research & Treatment 4, no. 1 (February 2005): 11–21. http://dx.doi.org/10.1177/153303460500400103.
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It is important to determine a breast cancer tumor target size for new screening equipment and molecular detection. Records of women aged 40–69 years diagnosed in 1988–1997 with a nonmetastasized, node-negative, or node-positive T1-stage breast cancer were abstracted from the Surveillance, Epidemiology, and End Results (SEER) public-use database. The linear, Gompertzian, lognormal, and power-exponential models of the effect of tumor size on breast cancer specific mortality were compared using corresponding transforms of size in multivariate Cox proportional hazard models. Criteria for comparison were the linearization of the size transforms and the Nagelkerke R2 N index for the Cox models. Our results show that the assumption of a linear effect of tumor size was rejected by the linearity test ( P=0.05). The Gompertzian, lognormal, and power-exponential transforms satisfied the test with P-values of 0.08, 0.29, and 0.14, respectively. The corresponding R2 N were 0.08410, 0.08420, and 0.08414, respectively, showing a marginally best fit with the lognormal model, which was selected as a model for small tumors. The lognormal function with unadjusted crude death rates gave a lognormal-location parameter of 25 and shape parameter of 1.7, while the corresponding values in multivariate models were 18 and 2, respectively. The derivation of the lognormal model indicates tumor growth acceleration starting at 3 mm (unadjusted crude data) or 2 mm (multivariate model). The breast cancer tumor target size for screening equipment, whether by imaging or molecular detection, is therefore 2 mm.
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Wang, Lei, Shibo Fang, Zhifang Pei, Yongchao Zhu, Dao Nguyen Khoi, and Wei Han. "Using FengYun-3C VSM Data and Multivariate Models to Estimate Land Surface Soil Moisture." Remote Sensing 12, no. 6 (March 24, 2020): 1038. http://dx.doi.org/10.3390/rs12061038.
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Land surface soil moisture (SM) monitoring is crucial for global water cycle and agricultural dryness research. The FengYun-3C Microwave Radiation Imager (FY-3C/MWRI) collects various Earth geophysical parameters, and the FY-3C/MWRI SM product (FY-3C VSM) has been widely applied to determine regional-scale surface SM contents. The FY-3C VSM retrieval accuracy in different seasons was evaluated by calculating the root mean square error (RMSE), unbiased RMSE (ubRMSE), mean absolute error (MAE), and correlation coefficient (R) values between the retrieved and measured SM. A lower accuracy in July (RMSE = 0.164 cm3/cm3, ubRMSE = 0.130 cm3/cm3, and MAE = 0.120 cm3/cm3) than in the other months was found due to the impacts of vegetation and climate variations. To show a detailed relationship between SM and multiple factors, including vegetation coverage, location, and elevation, quantile regression (QR) models were used to calculate the correlations at different quantiles. Except for the elevation at the 0.9 quantile, the QR models of the measured SM with the FY-3C VSM, MODIS NDVI, latitude, and longitude at each quantile all passed the significance test at the 0.005 level. Thus, the MODIS NDVI, latitude, and longitude were selected for error correction during the surface SM retrieval process using FY-3C VSM. Multivariate linear regression (MLR) and multivariate back-propagation neural network (MBPNN) models with different numbers of input variables were built to improve the SM monitoring results. The MBPNN model with three inputs (MBPNN-3) achieved the highest R (0.871) and lowest RMSE (0.034 cm3/cm3), MAE (0.026 cm3/cm3), and mean relative error (MRE) (20.7%) values, which were better than those of the MLR models with one, two, or three independent variables (MLR-1, -2, -3) and those of the MBPNN models with one or two inputs (MBPNN-1, -2). Then, the MBPNN-3 model was applied to generate the regional SM in the United States from January 2019 to October 2019. The estimated SM images were more consistent with the measured SM than the FY-3C VSM. This work indicated that combining FY-3C VSM data with the MBPNN-3 model could provide precise and reliable SM monitoring results.
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Vrac, Mathieu, and Petra Friederichs. "Multivariate—Intervariable, Spatial, and Temporal—Bias Correction*." Journal of Climate 28, no. 1 (December 31, 2014): 218–37. http://dx.doi.org/10.1175/jcli-d-14-00059.1.
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Abstract Statistical methods to bias correct global or regional climate model output are now common to get data closer to observations in distribution. However, most bias correction (BC) methods work for one variable and one location at a time and basically reproduce the temporal structure of the models. The intervariable, spatial, and temporal dependencies of the corrected data are usually poor compared to observations. Here, the authors propose a novel method for multivariate BC. The empirical copula–bias correction (EC–BC) combines a one-dimensional BC with a shuffling technique that restores an empirical multidimensional copula. Several BC methods are investigated and compared to high-resolution reference data over the French Mediterranean basin: notably, (i) a 1D BC method applied independently to precipitation and temperature fields, (ii) a recent conditional correction approach developed for producing correct two-dimensional intervariable structures, and (iii) the EC–BC method. Assessments are realized in terms of intervariable, spatial, and temporal dependencies, and an objective evaluation using the integrated quadratic distance (IQD) is presented. As expected, the 1D methods cannot produce correct multidimensional properties. The conditional technique appears efficient for intervariable properties but not for spatial and temporal dependencies. EC–BC provides realistic dependencies in all respects: intervariable, spatial, and temporal. The IQD results are clearly in favor of EC–BC. As many BC methods, EC–BC relies on a stationarity assumption and is only able to reproduce patterns inherited from historical data. However, because of its ease of coding, its speed of application, and the quality of its results, the EC–BC method is a very good candidate for all needs in multivariate bias correction.
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Eladoumikdachi, F. G., M. Jones, A. Shidfar, I. B. Helenowski, J. M. Franz, D. Scholtens, M. E. Sullivan, et al. "Prognostic classification of ipsilateral breast tumor recurrence (IBTR)." Journal of Clinical Oncology 29, no. 27_suppl (September 20, 2011): 137. http://dx.doi.org/10.1200/jco.2011.29.27_suppl.137.
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137 Background: IBTR after breast conservation encompasses true recurrence (TR) and new primary cancer (NP). No clear criteria distinguish TR from NP, but there is agreement that NP tumors have better outcomes than TR. Prior studies have used distance of IBTR from index cancer (IC), time to IBTR, histological, immunohistochemical (IHC) and genetic differences, but all data are not often available. We have examined IBTR patterns with the goal of identifying the simplest, most robust determinants of outcomes following IBTR. Methods: We reviewed records of breast cancer patients diagnosed with IBTR at the Lynn Sage Breast Center from 8/1992 to 6/2010. Data for the IBTR and IC were reviewed for histology, IHC, location, time between IC and IBTR, follow-up status, and cause of death. Parameters were scored as 1 if IBTR and IC were similar, and 0 if different (location=1 if ≤3cm; IHC=1 if hormone receptors and HER2 similar; interval=1 if ≤ 4 years). Univariate and multivariate proportional hazard models were used to determine impact on overall survival (OS), disease-specific survival (DSS), recurrence-free survival (RFS), distant recurrence free survival (DRFS) and local recurrence (LR). The multivariate model included significant univariate parameters. Results: We identified 161 patients with IBTR and complete data on ≥3 parameters; post-IBTR median follow up was 25 months. Data were missing on location in 13%, histology in 9%, IHC in 26%, and time interval in 0%. In univariate analysis, short interval to IBTR significantly decreased OS (HR 2.56, p=0.04), DSS (HR 4.31, p=0.009), RFS (HR 2.25, p=0.01), DRFS (HR 2.53, p=0.02), LR (HR 2.28, p=0.02); close location of IBTR decreased OS (HR 2.68, p=0.04). Histology and receptor status had no significant impact on the outcomes. Multivariate analysis included time and location, time ≤ 4 years was shown to decrease DSS (HR 4.00, p= 0.04), RFS (HR 2.32, p=0.03) and LR (HR 2.41, p=0.03). Conclusions: A short time interval between IC and IBTR is the most important prognostic parameter; location of IBTR within 3 cm of the IC also increases HR of subsequent events. These are the most easily available parameters when evaluating patients with IBTR, and therefore the most useful for distinction of TR versus new primary.
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Magill, Stephen T., Jacob S. Young, Ricky Chae, Manish K. Aghi, Philip V. Theodosopoulos, and Michael W. McDermott. "Relationship between tumor location, size, and WHO grade in meningioma." Neurosurgical Focus 44, no. 4 (April 2018): E4. http://dx.doi.org/10.3171/2018.1.focus17752.
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OBJECTIVEPrior studies have investigated preoperative risk factors for meningioma; however, no association has been shown between meningioma tumor size and tumor grade. The objective of this study was to investigate the relationship between tumor size and grade in a large single-center study of patients undergoing meningioma resection.METHODSA retrospective chart review of patients undergoing meningioma resection at the University of California, San Francisco, between 1985 and 2015 was performed. Patients with incomplete information, spinal meningiomas, multiple meningiomas, or WHO grade III meningiomas were excluded. The largest tumor dimension was used as a surrogate for tumor size. Univariate and multivariate logistic regression models were used to investigate the relationship between tumor grade and tumor size. A recursive partitioning analysis was performed to identify groups at higher risk for atypical (WHO grade II) meningioma.RESULTSOf the 1113 patients identified, 905 (81%) had a WHO grade I tumor and in 208 (19%) the tumors were WHO grade II. The median largest tumor dimension was 3.6 cm (range 0.2–13 cm). Tumors were distributed as follows: skull base (n = 573, 51%), convexity/falx/parasagittal (n = 431, 39%), and other (n = 109, 10%). On univariate regression, larger tumor size (p < 0.001), convexity/falx/parasagittal location (p < 0.001), and male sex (p < 0.001) were significant predictors of WHO grade II pathology. After controlling for interactions, multivariate regression found male sex (OR 1.74, 95% CI 1.25–2.43), size 3–6 cm (OR 1.69, 95% CI 1.08–2.66), size > 6 cm (OR 3.01, 95% CI 1.53–5.94), and convexity/falx/parasagittal location (OR 1.83, 95% CI 1.19–2.82) to be significantly associated with WHO grade II. Recursive partitioning analysis identified male patients with tumors > 3 cm as a high-risk group (32%) for WHO grade II meningioma.CONCLUSIONSLarger tumor size is associated with a greater likelihood of a meningioma being WHO grade II, independent of tumor location and male sex, which are known risk factors.
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López-Espejo, Mauricio, and Marta Hernández-Chávez. "Could infarct location predict the long-term functional outcome in childhood arterial ischemic stroke?" Arquivos de Neuro-Psiquiatria 75, no. 10 (October 2017): 692–96. http://dx.doi.org/10.1590/0004-282x20170124.
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ABSTRACT Objective: To explore the influence of infarct location on long-term functional outcome following a first-ever arterial ischemic stroke (AIS) in non-neonate children. Method: The MRIs of 39 children with AIS (median age 5.38 years; 36% girls; mean follow-up time 5.87 years) were prospectively evaluated. Infarct location was classified as the absence or presence of subcortical involvement. Functional outcome was measured using the modified Rankin scale (mRS) for children after the follow-up assessment. We utilized multivariate logistic regression models to estimate the odds ratios (ORs) for the outcome while adjusting for age, sex, infarct size and middle cerebral artery territory involvement (significance < 0.05). Results: Both infarcts ≥ 4% of total brain volume (OR 9.92; CI 1.76 – 55.9; p 0.009) and the presence of subcortical involvement (OR 8.36; CI 1.76 – 53.6; p 0.025) independently increased the risk of marked functional impairment (mRS 3 to 5). Conclusion: Infarct extension and location can help predict the extent of disability after childhood AIS.
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Medina, Víctor Damián, and Andrés Niembro=. "Towards a model of Latin American tourist cities? The case of San Carlos de Bariloche, Argentina." International Journal of Tourism Cities 6, no. 4 (July 2, 2020): 975–98. http://dx.doi.org/10.1108/ijtc-02-2020-0019.
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Purpose Taking as a case study the city of San Carlos de Bariloche – in northern Patagonia, Argentina – this paper aims to compare its urban structure with previous urbanization models and identify some characteristics of this tourist city that could inspire the construction of an adapted urban model for Latin American tourist cities, particularly those based on natural attractions. Design/methodology/approach Based on multivariate analysis of population census data and local economic statistics, this paper compares the residential location of different social groups and the location of main economic activities in Bariloche. First, principal component analysis (PCA) is combined with cluster analysis to classify Bariloche’s neighborhoods. Second, different maps are analyzed to study the location of economic activities, in comparison with previous clusters. Findings The results of this paper show that Bariloche partially adjusts to previous urbanization models, as the landscape and physical environment determine the characteristics of its urban growth, as well as the development of tourist activities. Therefore, this paper then proposes an adapted urban model for the case of Bariloche, which could be also contrasted with other Latin American tourist cities in the future. Originality/value Bearing in mind that there is no model of Latin American tourist cities so far, this paper tries to analyze to what extent the assumptions and patterns of previous urban models could be adapted to Latin American tourist cities, such as Bariloche, which base their attractiveness and economic dynamism on its natural physical environment.
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Brunner, Manuela I., Reinhard Furrer, and Anne-Catherine Favre. "Modeling the spatial dependence of floods using the Fisher copula." Hydrology and Earth System Sciences 23, no. 1 (January 8, 2019): 107–24. http://dx.doi.org/10.5194/hess-23-107-2019.
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Abstract. Floods often affect not only a single location, but also a whole region. Flood frequency analysis should therefore be undertaken at a regional scale which requires the considerations of the dependence of events at different locations. This dependence is often neglected even though its consideration is essential to derive reliable flood estimates. A model used in regional multivariate frequency analysis should ideally consider the dependence of events at multiple sites which might show dependence in the lower and/or upper tail of the distribution. We here seek to propose a simple model that on the one hand considers this dependence with respect to the network structure of the region and on the other hand allows for the simulation of stochastic event sets at both gauged and ungauged locations. The new Fisher copula model is used for representing the spatial dependence of flood events in the nested Thur catchment in Switzerland. Flood event samples generated for the gauged stations using the Fisher copula are compared to samples generated by other dependence models allowing for modeling of multivariate data including elliptical copulas, R-vine copulas, and max-stable models. The comparison of the dependence structures of the generated samples shows that the Fisher copula is a suitable model for capturing the spatial dependence in the data. We therefore use the copula in a way such that it can be used in an interpolation context to simulate event sets comprising gauged and ungauged locations. The spatial event sets generated using the Fisher copula well capture the general dependence structure in the data and the upper tail dependence, which is of particular interest when looking at extreme flood events and when extrapolating to higher return periods. The Fisher copula was for a medium-sized catchment found to be a suitable model for the stochastic simulation of flood event sets at multiple gauged and ungauged locations.
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Jhawar, Balraj S., Adrianna Ranger, David A. Steven, and Rolando F. Del Maestro. "A Follow-up Study of Infants with Intracranial Hemorrhage at Full-Term." Canadian Journal of Neurological Sciences / Journal Canadien des Sciences Neurologiques 32, no. 3 (August 2005): 332–39. http://dx.doi.org/10.1017/s0317167100004224.
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ABSTRACT:Objective:To determine physical and cognitive outcomes of full-term infants who suffered intracranial hemorrhage (ICH) at birth.Methods:A retrospective hospital-based, follow-up study of infants treated in London, Ontario between 1985 and 1996. Follow-up was conducted by telephone interviews and clinic visits. Outcome was measured according to physical and cognitive scales. Perinatal risk factors and hemorrhage characteristics were correlated with final outcome.Results:For this study 66 infants with ICH were identified, of which seven died during the first week of life. We obtained follow-up in all but ten cases (median = 3-years; range 1.0 to 10.9 years). Overall, 57% of infants had no physical or cognitive deficits at follow-up. Death occurred most frequently among those with primarily subarachnoid hemorrhage (19%) and the most favorable outcomes occurred among those with subdural hemorrhage (80% had no disability). In univariate models, thrombocytopenia (platelet count ≤ 70 x 109/L), increasing overall hemorrhage severity, frontal location and spontaneous vaginal delivery as opposed to forceps-assisted delivery increased risk for poor outcome. In multivariate models, all these factors tended towards increased risk, but only thrombocytopenia remained significant for physical disability (OR = 7.6; 95% CI = 1.02 – 56.6); thrombocytopenia was borderline significant in similar models for cognitive disability (OR = 4.6; 95% CI = 0.9 – 23.9).Conclusion:Although forceps-assisted delivery may contribute to ICH occurrence, our study found better outcomes among these infants than those who had ICH following a spontaneous vaginal delivery. Hemorrhage in the frontal lobe was the most disabling hemorrhage location and if multiple compartments were involved, disability was also more likely to occur. However, in this report we found that the factor that was most likely to contribute to poor outcome was thrombocytopenia and this remained important in multivariate analysis.
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Lian, Lijin, Xuejuan Hu, Zhenhong Huang, Liang Hu, and Lu Xu. "Pigment analysis based on a line-scanning fluorescence hyperspectral imaging microscope combined with multivariate curve resolution." PLOS ONE 16, no. 8 (August 9, 2021): e0254864. http://dx.doi.org/10.1371/journal.pone.0254864.
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A rapid and cost-effective system is vital for the detection of harmful algae that causes environmental problems in terms of water quality. The approach for algae detection was to capture images based on hyperspectral fluorescence imaging microscope by detecting specific fluorescence signatures. With the high degree of overlapping spectra of algae, the distribution of pigment in the region of interest was unknown according to a previous report. We propose an optimization method of multivariate curve resolution (MCR) to improve the performance of pigment analysis. The reconstruction image described location and concentration of the microalgae pigments. This result indicated the cyanobacterial pigment distribution and mapped the relative pigment content. In conclusion, with the advantage of acquiring two-dimensional images across a range of spectra, HSI conjoining spectral features with spatial information efficiently estimated specific features of harmful microalgae in MCR models.
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Jiang, Mengjie, Yinuo Tan, Xiaofen Li, Jianfei Fu, Hanguang Hu, Xianyun Ye, Ying Cao, Jinghong Xu, and Ying Yuan. "Clinicopathological Features and Prognostic Factors of Colorectal Neuroendocrine Neoplasms." Gastroenterology Research and Practice 2017 (2017): 1–9. http://dx.doi.org/10.1155/2017/4206172.
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Background. Limited research is available regarding colorectal NENs and the prognostic factors remain controversial. Materials and Methods. A total of 68 patients with colorectal NENs were studied retrospectively. Clinical characteristics and prognosis between colonic and rectal NENs were compared. The Cox regression models were used to evaluate the predictive capacity. Results. Of the 68 colorectal NENs patients, 43 (63.2%) had rectal NENs, and 25 (36.8%) had colonic NENs. Compared with rectal NENs, colonic NENs more frequently exhibited larger tumor size (P<0.0001) and distant metastasis (P<0.0001). Colonic NENs had a worse prognosis (P=0.027), with 5-year overall survival rates of 66.7% versus 88.1%. NET, NEC, and MANEC were noted in 61.8%, 23.5%, and 14.7% of patients, respectively. Multivariate analyses revealed that tumor location was not an independent prognostic factor (P=0.081), but tumor size (P=0.037) and pathological classification (P=0.012) were independent prognostic factors. Conclusion. Significant differences exist between colonic and rectal NENs. Multivariate analysis indicated that tumor size and pathological classification were associated with prognosis. Tumor location was not an independent factor. The worse outcome of colonic NENs observed in clinical practice might be due not only to the biological differences, but also to larger tumor size in colonic NENs caused by the delayed diagnosis.
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Li, Jinzhong. "Analysis on the Relationship between Financing Constraints and Research and Development from the Perspective of the Location of Top Management Network." Discrete Dynamics in Nature and Society 2022 (January 6, 2022): 1–11. http://dx.doi.org/10.1155/2022/8690801.
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Taking listed Chinese companies during 2009–2019 as objects, this paper constructs a multivariate discriminant model to measure the degrees of multiple financing constraints and establishes empirical models to analyze the non-linear relationship between the financing constraints and research and development (R&D) investment. Further, the author investigated how the top management network (TMN) location acts on the relationship between financing constraints and R&D investment. The research provides a robust evidence to an inverted U-shaped relationship between the degrees of financing constraints and corporate R&D investment: appropriate financing constraints promote corporate R&D investment; once passing a turning point, excess financing constraints would suppress corporate R&D investment. Besides, it was learned that TMN location positively moderates the financing constraints and R&D investment. In addition, TMN location plays a more obvious regulating role in non-state-owned enterprises (non-SOEs) than in SOEs. The research clarifies the relationship between financing constraints and R&D investment, as well as the moderating role of TMN location. Empirical evidence was provided to help the government reduce credit discrimination and enterprises to widen financing channels and improve innovation capability.
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De La Mora-Orozco, Celia, José G. Flores-Garnica, Lucia M. Vega-Ramírez, Irma J. González-Acuña, Juan Nápoles-Armenta, and Edgardo Martínez-Orozco. "Total Organic Carbon Assessment in Soils Cultivated with Agave tequilana Weber in Jalisco, Mexico." Sustainability 13, no. 1 (December 28, 2020): 208. http://dx.doi.org/10.3390/su13010208.
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The Agave tequilana Weber is an important commercial crop in the State of Jalisco, Mexico. However, the agave cultivation generates significant soil loss. For that reason, knowledge about the implementation of the agriculture management practices, such as manure application and the combination of inorganic fertilizers and manure, are relevant. The objective of this research was to determine the effect of agricultural management practices on the total organic carbon (TOC) in the soil in three study locations: Arandas, Tepatitlán, and Acatic in the Altos Sur region of Jalisco. A random sampling was carried out in each study location, 12 samples were obtained for each location at 0–30 cm deep, and a total of 36 samples were analyzed. The evaluated parameters were the potential hydrogen (pH), electrical conductivity (EC), bulk density (BD), soil-water saturation (SWS), total nitrogen (TN), and total organic carbon (TOC). Basic statistics and correlations between parameters were generated. In addition, to estimate TOC from a multivariate analysis, models were developed based on the lowest Akaike information criterion (AIC) and of the classification and regression trees (CART). ANOVA and Tukey test were determined. Results demonstrated a significant difference in the TOC percentages between the study locations. The Tukey test showed that there is no difference in TOC content between the Tepatitlán and Arandas sites, but there is a difference between these two sites and the Acatic. The latter resulted with the lowest values of TOC. Long-term studies are recommended to develop crop management strategies.
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Choiriyah, Evita, Utami Dyah Syafitri, and I. Made Sumertajaya. "PENGEMBANGAN MODEL PERAMALAN SPACE TIME." Indonesian Journal of Statistics and Its Applications 4, no. 4 (December 25, 2020): 579–89. http://dx.doi.org/10.29244/ijsa.v4i4.584.
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Based on Statistics Indonesia (BPS) South Sulawesi is one of the national rice granary province. There are three regions, Bone, Wajo, and Gowa that contribute to the high production of rice in South Sulawesi. However, rice production in Indonesia especially South Sulawesi often declined sharply due to climate disturbances, such as drought or flood. Therefore, Indonesia's government should provide a forecast related to rice production accurately to ensure the availability of food stocks as an integral part of national food security. Moreover, rainfall as climate factors should be included to produce an appropriate forecast model that can be expected to generate the estimation of the rice production data accurately. This research focused on comparing the forecasting model of rice production data by SARIMAX and GSTARIMAX model and used rainfall as explanatory variables. The SARIMAX model is a multivariate time series forecasting model that can accommodate the seasonal components. In contrast, the GSTARIMAX model, which is equipped with an inverse distance spatial weight matrix, is a space-time forecasting model that involves interconnection between locations. The GSTARIMAX model built for rice production forecasting in Bone, Wajo, and Gowa is GSTARIMAX (2,1,0)(0,1,1)12. Rainfall as an explanatory variable was significant at each location. The comparison of rice production forecasting models for the next six periods in four locations showed that the GSTARIMAX model provided more stable forecasting results than the SARIMAX model, viewed from the average MAPE value of the GSTARIMAX mode in each location.
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Yamada, Walter M., Michael N. Neely, Jay Bartroff, David S. Bayard, James V. Burke, Mike van Guilder, Roger W. Jelliffe, et al. "An Algorithm for Nonparametric Estimation of a Multivariate Mixing Distribution with Applications to Population Pharmacokinetics." Pharmaceutics 13, no. 1 (December 30, 2020): 42. http://dx.doi.org/10.3390/pharmaceutics13010042.
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Population pharmacokinetic (PK) modeling has become a cornerstone of drug development and optimal patient dosing. This approach offers great benefits for datasets with sparse sampling, such as in pediatric patients, and can describe between-patient variability. While most current algorithms assume normal or log-normal distributions for PK parameters, we present a mathematically consistent nonparametric maximum likelihood (NPML) method for estimating multivariate mixing distributions without any assumption about the shape of the distribution. This approach can handle distributions with any shape for all PK parameters. It is shown in convexity theory that the NPML estimator is discrete, meaning that it has finite number of points with nonzero probability. In fact, there are at most N points where N is the number of observed subjects. The original infinite NPML problem then becomes the finite dimensional problem of finding the location and probability of the support points. In the simplest case, each point essentially represents the set of PK parameters for one patient. The probability of the points is found by a primal-dual interior-point method; the location of the support points is found by an adaptive grid method. Our method is able to handle high-dimensional and complex multivariate mixture models. An important application is discussed for the problem of population pharmacokinetics and a nontrivial example is treated. Our algorithm has been successfully applied in hundreds of published pharmacometric studies. In addition to population pharmacokinetics, this research also applies to empirical Bayes estimation and many other areas of applied mathematics. Thereby, this approach presents an important addition to the pharmacometric toolbox for drug development and optimal patient dosing.
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Diluna, M. L., J. T. King, J. P. Knisely, J. E. Bond, A. C. De Lotbiniere, and V. L. Chiang. "Multivariate analysis identifies factors that affect survival after stereotactic radiosurgery for brain metastases." Journal of Clinical Oncology 24, no. 18_suppl (June 20, 2006): 11500. http://dx.doi.org/10.1200/jco.2006.24.18_suppl.11500.
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11500 Background: Stereotactic radiosurgery (SRS) has become a standard for the treatment of brain metastases. We attempted to determine if specific tumor- or patient-related factors in our population independently predicted better survival. Methods: Survival data for all 334 patients with brain metastases treated with SRS between Jan 1998 and Dec 2004 were analyzed. Clinical data were abstracted retrospectively from treatment records; survival data were obtained from the Connecticut Tumor Registry. Kaplan-Meier plots and Cox proportional hazard multivariate regression models were used to identify the factors that independently affected survival. Variables analyzed included age, sex, race, histology, number, location and total volume of metastatic lesions, surgical resection, WBXRT, chemotherapy, and systemic disease control. Results: Median age of our patient population was 57.3 years. The median number of lesions treated in a single session was 2 (range 1 to 36). Tumor histologies included non-small cell lung carcinoma (36%), breast (17%), melanoma (16%), small cell lung carcinoma (8%), renal cell (8%), esophageal (2%), and other (16%). Three hundred patients (90%) had confirmed deaths, with a median survival after SRS of 8.1 months. Increased survival was independently associated with systemic control (HR = 0.51, P<0.001), breast cancer (HR = 0.60, P=0.003), and total tumor volume <5cc (HR = 0.68, P=0.003). Decreased survival was independently associated with the presence of four or more cerebral metastases (HR = 1.50, P=0.005) and a trend toward decreased survival was associated with esophageal cancer (HR = 2.24, P=0.055). There was no difference in survival associated with age, race, sex, location of metastases, surgical resection, WBXRT before or after SRS, or chemotherapy. Conclusions: Breast cancer, systemic control, and fewer metastases were all significant independent predictors of improved survival. Of note, histological diagnosis, other than breast or esophageal cancer, did not affect patient survival, and a total tumor burden of 5cc or more was associated with worse survival independent of the number of metastases. These results should assist in survival prognostication in patients with cerebral metastases considering SRS. No significant financial relationships to disclose.
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Odeny, Thomas A., Nicole Farha, Wilfred Vazquez, Amna Batool, Anwaar Saeed, Ravi Kumar Paluri, and Anup Kasi. "Association between primary tumor site, perioperative CEA ratio, and overall survival in patients with colorectal cancer." Journal of Clinical Oncology 37, no. 4_suppl (February 1, 2019): 518. http://dx.doi.org/10.1200/jco.2019.37.4_suppl.518.
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518 Background: There are differences in the incidence, clinical presentation, molecular pathogenesis and outcome of colorectal cancer (CRC) based on the tumor location. Emerging research suggests that the perioperative carcinoembryonic antigen (CEA) ratio is a prognostic factor for CRC patients. We aimed to determine the association between tumor location, perioperative CEA ratio, and 5-year survival among patients with CRC. Methods: We analyzed 111 patients who underwent resection for CRC at KUMC. After excluding patients without pre- or post-operative CEA data, 62 patients for whom we could calculate a CEA ratio (post-op/pre-op CEA) were classified as either high ( ≥ 0.5) or low ( < 0.5) ratio. The primary outcomes were: 1) overall survival (OS) stratified by tumor location; 2) OS stratified by CEA ratio; and 3) whether there was effect modification by tumor location, of the association between perioperative CEA ratio and OS, after adjusting for tumor stage and smoking status. Kaplan-Meier method was used to estimate survival rates, and Cox proportional hazards models for multivariate analysis. Results: The median age was 61 years, 54% male, 31% smokers, 74% left-sided tumors, median pre-operative CEA was 3.3, and 60% had CEA ratio ≥ 0.5. The OS rates were 89.1% and 81.3% in patients with left-sided versus right-sided tumors respectively (p-value = 0.4). The OS rates were 83.8% and 92.0% in patients with high versus low CEA ratios respectively (p-value = 0.3). There was effect modification by tumor location on association between CEA ratio and OS, after adjusting for smoking status and tumor stage (p-value < 0.001). However, in the stratified analysis, the n was too small to permit inferential analysis. In multivariate analysis, both tumor location (HR 0.4; p = 0.3) and perioperative CEA ratio (HR 2.7; p = 0.3) were not significantly associated with OS after adjusting for smoking status and tumor stage. Conclusions: There was no difference in OS between left versus right-sided tumors. The association between perioperative CEA ratio and OS was significantly modified by tumor location. However, to attribute this modification to left versus right warrants validation in a larger cohort as our sample size was limited.
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Rauhut, Alexander. "Exploring the Effect of Conversion on the Distribution of Inflectional Suffixes: A Multivariate Corpus Study." Zeitschrift für Anglistik und Amerikanistik 69, no. 3 (September 1, 2021): 267–90. http://dx.doi.org/10.1515/zaa-2021-2024.
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Abstract Lexical ambiguity in the English language is abundant. Word-class ambiguity is even inherently tied to the productive process of conversion. Most lexemes are rather flexible when it comes to word class, which is facilitated by the minimal morphology that English has preserved. This study takes a multivariate quantitative approach to examine potential patterns that arise in a lexicon where verb-noun and noun-verb conversion are pervasive. The distributions of three inflectional suffixes, verbal -s, nominal -s, and -ed are explored for their interaction with degrees of verb-noun conversion. In order to achieve that, the lexical dispersion, context-dependency, and lexical similarity between the inflected and bare forms were taken into consideration and controlled for in a Generalized Additive Models for Location, Scale and Shape (GAMLSS; Stasinopoulos, M. D., R. A. Rigby, and F. De Bastiani. 2018. “GAMLSS: A Distributional Regression Approach.” Statistical Modelling 18 (3–4): 248–73). The results of a series of zero-one-inflated beta models suggest that there is a clear “uncanny” valley of lexemes that show similar proportions of verbal and nominal uses. Such lexemes have a lower proportion of inflectional uses when textual dispersion and context-dependency are controlled for. Furthermore, as soon as there is some degree of conversion, the probability that a lexeme is always encountered without inflection sharply rises. Disambiguation by means of inflection is unlikely to play a uniform role depending on the inflectional distribution of a lexeme.
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Patel-Dovlatabadi, Payal. "Factors associated with physicians' prescribing behavior for treatment of influenza in the USA." International Journal of Pharmaceutical and Healthcare Marketing 8, no. 1 (April 1, 2014): 27–46. http://dx.doi.org/10.1108/ijphm-01-2013-0002.
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Purpose – The aim of this paper is to identify factors (i.e. age, gender, ethnicity, type of medical facility, geographical location, etc.) associated with physicians' prescribing behavior when treating influenza in the USA. The study aims to examine why the number of antiviral prescriptions remains substandard. Design/methodology/approach – Data were obtained from the National Ambulatory Medical Care Survey for each influenza season between the years of 2005-2008. Bivariate analyses and two models of multivariate logistic regression analyses (one with no fixed effect and the other including year as a fixed effect) were used to analyze the data. Findings – The results from this study revealed that among family practice physicians, 40.5 percent prescribed antiviral medications to patients presenting with influenza while 59.5 percent prescribed another form of medication. Antibiotics comprised 41.3 percent of the prescriptions for treatment of influenza. Multivariable logistic regression analyses revealed that race (White; p=0.023), type of health setting (private solo/group practice; p=0.041), employment status (owner; p=0.046), and metropolitan location (metropolitan statistical area; p=0.032) were all significantly associated with prescribing antivirals. Patients' expected source of payment (private insurance) and geographical location (Midwest) of health facility were marginally associated with prescribing antivirals. Originality/value – By identifying factors associated with physicians' prescribing practices of antiviral medications, a more timely diagnosis and treatment of influenza can occur. Efforts should be targeted to improve physician education and awareness of the illness. Interventions may be implemented to improve the prescribing of antiviral medications and potentially inappropriate prescribing.
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Pettus, J., D. Sharp, Y. Ofer, A. Bach, and P. Russo. "Tumor location does not impact return of renal function following partial nephrectomy." Journal of Clinical Oncology 24, no. 18_suppl (June 20, 2006): 14511. http://dx.doi.org/10.1200/jco.2006.24.18_suppl.14511.
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14511 Background: To study the impact of tumor location on the glomerular filtration rate (GFR) changes following partial nephrectomy. Methods: We reviewed our institutional database to identify patients who underwent partial nephrectomy between 1/1995 and 7/2005. Preoperative CT and/or MRI studies were reviewed to characterize tumors as either central or peripheral. Central tumors were defined as those involving the collecting system or renal sinus; all others were categorized as peripheral. We used the Abbreviated Modification of Diet in Renal Disease study equation to estimate GFR preoperatively, in the early postoperative hospital stay, and at 1 and 12 months after surgery. Multivariate models were fit to determine the association of tumor location with changes in GFR at each time period after controlling for age, sex, tumor size, American Society of Anesthesia Score, ischemic time, operative time, and blood loss. Results: A total of 616 patients (265 central and 351 peripheral tumors) were available for analysis. Patients with central tumors were younger compared to those with peripheral tumors (62 vs. 59, p = 0.014), had longer intraoperative renal ischemia (40 vs. 30 min, respectively, p < 0.001), and had longer operations (201 vs. 184 min, respectively, p = 0.01). Although baseline GFR did not differ between the groups, a significantly larger decrease in GFR was found in patients with central compared to peripheral tumors in the early postoperative period (−16 vs. −11 cc/min/1.73 m2, p = 0.013) and 1-month follow-up (−10 vs. −6 cc/min/1.73 m2, p = 0.017). The GFR change was similar at 1-year follow-up, −10 and −11 cc/min/1.73m2 (p = 0.586) for central and peripheral tumors, respectively. On multivariate analysis, tumor location was not significantly associated with the change in GFR at any of the time intervals after adjusting for size, ischemic time, operative time, age, sex and comorbidity. Conclusions: Patients with centrally located tumors have a more pronounced short-term decrease in GFR, which reflects a longer operative time and ischemia duration. Tumor location does not appear to impact the long-term renal function. This suggests that renal sparing surgery should not be withheld from this subset of patients. No significant financial relationships to disclose.
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Xin, Seng Jia, and Kamil Khalid. "Modelling House Price Using Ridge Regression and Lasso Regression." International Journal of Engineering & Technology 7, no. 4.30 (November 30, 2018): 498. http://dx.doi.org/10.14419/ijet.v7i4.30.22378.
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House price prediction is important for the government, finance company, real estate sector and also the house owner. The data of the house price at Ames, Iowa in United State which from the year 2006 to 2010 is used for multivariate analysis. However, multicollinearity is commonly occurred in the multivariate analysis and gives a serious effect to the model. Therefore, in this study investigates the performance of the Ridge regression model and Lasso regression model as both regressions can deal with multicollinearity. Ridge regression model and Lasso regression model are constructed and compared. The root mean square error (RMSE) and adjusted R-squared are used to evaluate the performance of the models. This comparative study found that the Lasso regression model is performing better compared to the Ridge regression model. Based on this analysis, the selected variables includes the aspect of house size, age of house, condition of house and also the location of the house.