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Journal articles on the topic 'Generalized Information Criterion (GIC)'

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Author: Grafiati

Published: 10 December 2022

Last updated: 28 January 2023

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1

Xie, Qichang, and Meng Du. "The Optimal Selection for Restricted Linear Models with Average Estimator." Abstract and Applied Analysis 2014 (2014): 1–13. http://dx.doi.org/10.1155/2014/692472.

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The essential task of risk investment is to select an optimal tracking portfolio among various portfolios. Statistically, this process can be achieved by choosing an optimal restricted linear model. This paper develops a statistical procedure to do this, based on selecting appropriate weights for averaging approximately restricted models. The method of weighted average least squares is adopted to estimate the approximately restricted models under dependent error setting. The optimal weights are selected by minimizing ak-class generalized information criterion (k-GIC), which is an estimate of the average squared error from the model average fit. This model selection procedure is shown to be asymptotically optimal in the sense of obtaining the lowest possible average squared error. Monte Carlo simulations illustrate that the suggested method has comparable efficiency to some alternative model selection techniques.

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2

Wu, Lei Lei, and Nai Ping Cheng. "A Novel Channel Estimation Algorithm for Underwater Acoustic Systems." Applied Mechanics and Materials 416-417 (September 2013): 1309–13. http://dx.doi.org/10.4028/www.scientific.net/amm.416-417.1309.

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To decrease the computational complexity and improve the performance of channel estimation for underwater acoustic (UWA) sparse multipath channels, a sparse least square (SLS) channel estimation algorithm is proposed. The proposed algorithm combines advantages of both generalized akaike information criterion (GAIC) estimation and estimation using effective order of channel impulse response (CIR). Known pilot data is used to estimate effective order of CIR and then the position of taps of CIR is estimated. In order to adapt to the environment with low SNR and reduce the dimension of signal space, adaptive threshold is also used. Simulation results indicate that the proposed method has good performance of channel estimation and low complexity.

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3

Elhassan, Tomader. "Impact of Covid-19 pandemic on stock market returns volatility of Gulf Cooperation Council countries." Investment Management and Financial Innovations 18, no. 4 (October 13, 2021): 45–56. http://dx.doi.org/10.21511/imfi.18(4).2021.05.

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This study examined the asymmetric impact of the COVID-19 pandemic on the Gulf Cooperation Council (GCC) stock market return volatility. The data included daily closing prices of the GCC stock market from the day of the acknowledgment of the first case of COVID-19 in each country to March 6, 2021. In addition, the study employed generalized autoregressive conditional heteroscedasticity (GARCH) family models. According to the Akaike information criterion, GARCH and exponential GARCH (EGARCH) were the most accurate models. The findings of the GARCH model indicate that the COVID-19 pandemic affected the GCC stock markets. The EGARCH model also confirmed the impact of the COVID-19 pandemic on the GCC stock markets, confirming that the COVID-19 negatively affected GCC stock market returns. The value of the persistence of this volatility continued over a long period. This study has potential implications for investors and policymakers in diversifying investment portfolios and adopting strategies to maintain investor confidence during such crises. Moreover, mechanisms must be developed for reducing risks in financial markets in times of crisis, and central banks should take financial measures to mitigate risks to capital markets. AcknowledgmentsThis achievement was made with the aid of my family’s support, thank you all.

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4

Taniguchi, Masanobu, and Junichi Hirukawa. "Generalized information criterion." Journal of Time Series Analysis 33, no. 2 (September 1, 2011): 287–97. http://dx.doi.org/10.1111/j.1467-9892.2011.00759.x.

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5

Wang, L. "Wilcoxon-type generalized Bayesian information criterion." Biometrika 96, no. 1 (January 24, 2009): 163–73. http://dx.doi.org/10.1093/biomet/asn060.

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6

Yingbin, Gao, Kong Xiangyu, Hu Changhua, Li Hongzeng, and Hou Li'an. "A Generalized Information Criterion for Generalized Minor Component Extraction." IEEE Transactions on Signal Processing 65, no. 4 (February 15, 2017): 947–59. http://dx.doi.org/10.1109/tsp.2016.2631444.

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7

Zhang, Yiyun, Runze Li, and Chih-Ling Tsai. "Regularization Parameter Selections via Generalized Information Criterion." Journal of the American Statistical Association 105, no. 489 (March 1, 2010): 312–23. http://dx.doi.org/10.1198/jasa.2009.tm08013.

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8

Pan, Wei. "Akaike's Information Criterion in Generalized Estimating Equations." Biometrics 57, no. 1 (March 2001): 120–25. http://dx.doi.org/10.1111/j.0006-341x.2001.00120.x.

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9

Shao, Jun. "Convergence rates of the generalized information criterion." Journal of Nonparametric Statistics 9, no. 3 (January 1998): 217–25. http://dx.doi.org/10.1080/10485259808832743.

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10

Na, Okyoung. "Generalized information criterion for the AR model." Journal of the Korean Statistical Society 46, no. 1 (March 2017): 146–60. http://dx.doi.org/10.1016/j.jkss.2016.12.002.

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11

Chang, Minsu, and Francis J. DiTraglia. "A generalized focused information criterion for GMM." Journal of Applied Econometrics 33, no. 3 (January 11, 2018): 378–97. http://dx.doi.org/10.1002/jae.2614.

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12

Erdogmus, D., and J. C. Principe. "Generalized information potential criterion for adaptive system training." IEEE Transactions on Neural Networks 13, no. 5 (September 2002): 1035–44. http://dx.doi.org/10.1109/tnn.2002.1031936.

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13

MATSUBA, IKUO. "GENERALIZED INFORMATION CRITERION FOR LINEAR AND NONLINEAR PROCESSES." International Journal of Bifurcation and Chaos 12, no. 02 (February 2002): 389–95. http://dx.doi.org/10.1142/s0218127402004413.

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A generalized information criterion is proposed to determine an embedding dimension and a delay time for delay coordinates of the reconstructed dynamics both for linear stochastic and nonlinear deterministic processes. While the standard maximum likelihood type method requires statistical parametric models such as autoregressive models, the generalized information criterion is constructed from the quantity in accordance with the second-order Renyi entropy in terms of the correlation integral for the finite number of data which is directly obtained from a time delay vector. It is found numerically that the present method works well when applied to chaotic and stochastic systems.

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14

Xu, ChangJiang, and A. Ian McLeod. "Further asymptotic properties of the generalized information criterion." Electronic Journal of Statistics 6 (2012): 656–63. http://dx.doi.org/10.1214/12-ejs685.

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15

Lombardía, María José, Esther López-Vizcaíno, and Cristina Rueda. "Mixed generalized Akaike information criterion for small area models." Journal of the Royal Statistical Society: Series A (Statistics in Society) 180, no. 4 (June 21, 2017): 1229–52. http://dx.doi.org/10.1111/rssa.12300.

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16

Seo, Wangduk, Dae-Won Kim, and Jaesung Lee. "Generalized Information-Theoretic Criterion for Multi-Label Feature Selection." IEEE Access 7 (2019): 122854–63. http://dx.doi.org/10.1109/access.2019.2927400.

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17

Al-Marshadi, Ali Hussein, Abdullah Hamoud Alharby, and Muhammad Aslam. "Evaluating modified generalized information criterion in presence of multicollinearity." Communications in Statistics - Simulation and Computation 46, no. 8 (July 28, 2016): 6298–307. http://dx.doi.org/10.1080/03610918.2016.1202273.

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18

Yu, Dalei, and Kelvin K. W. Yau. "Conditional Akaike information criterion for generalized linear mixed models." Computational Statistics & Data Analysis 56, no. 3 (March 2012): 629–44. http://dx.doi.org/10.1016/j.csda.2011.09.012.

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19

Ito, Sosuke. "Information geometry, trade-off relations, and generalized Glansdorff–Prigogine criterion for stability." Journal of Physics A: Mathematical and Theoretical 55, no. 5 (January 17, 2022): 054001. http://dx.doi.org/10.1088/1751-8121/ac3fc2.

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Abstract We discuss a relationship between information geometry and the Glansdorff–Prigogine criterion for stability. For the linear master equation, we found a relation between the line element and the excess entropy production rate. This relation leads to a new perspective of stability in a nonequilibrium steady-state. We also generalize the Glansdorff–Prigogine criterion for stability based on information geometry. Our information-geometric criterion for stability works well for the nonlinear master equation, where the Glansdorff–Prigogine criterion for stability does not work well. We derive a trade-off relation among the fluctuation of the observable, the mean change of the observable, and the intrinsic speed. We also derive a novel thermodynamic trade-off relation between the excess entropy production rate and the intrinsic speed. These trade-off relations provide a physical interpretation of our information-geometric criterion for stability. We illustrate our information-geometric criterion for stability by an autocatalytic reaction model, where dynamics are driven by a nonlinear master equation.

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20

Sueishi, Naoya. "Generalized Empirical Likelihood-Based Focused Information Criterion and Model Averaging." Econometrics 1, no. 2 (July 3, 2013): 141–56. http://dx.doi.org/10.3390/econometrics1020141.

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21

Lu, Zhihua, and Abdelhak M. Zoubir. "Generalized Bayesian Information Criterion for Source Enumeration in Array Processing." IEEE Transactions on Signal Processing 61, no. 6 (March 2013): 1470–80. http://dx.doi.org/10.1109/tsp.2012.2232661.

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22

Zhang, Qingzhao, Xiaogang Duan, and Shuangge Ma. "Focused information criterion and model averaging with generalized rank regression." Statistics & Probability Letters 122 (March 2017): 11–19. http://dx.doi.org/10.1016/j.spl.2016.10.020.

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23

Pu, Wenji, and Xu-Feng Niu. "Selecting mixed-effects models based on a generalized information criterion." Journal of Multivariate Analysis 97, no. 3 (March 2006): 733–58. http://dx.doi.org/10.1016/j.jmva.2005.05.009.

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24

Voncken, Lieke, Casper J. Albers, and Marieke E. Timmerman. "Model Selection in Continuous Test Norming With GAMLSS." Assessment 26, no. 7 (June 29, 2017): 1329–46. http://dx.doi.org/10.1177/1073191117715113.

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To compute norms from reference group test scores, continuous norming is preferred over traditional norming. A suitable continuous norming approach for continuous data is the use of the Box–Cox Power Exponential model, which is found in the generalized additive models for location, scale, and shape. Applying the Box–Cox Power Exponential model for test norming requires model selection, but it is unknown how well this can be done with an automatic selection procedure. In a simulation study, we compared the performance of two stepwise model selection procedures combined with four model-fit criteria (Akaike information criterion, Bayesian information criterion, generalized Akaike information criterion (3), cross-validation), varying data complexity, sampling design, and sample size in a fully crossed design. The new procedure combined with one of the generalized Akaike information criterion was the most efficient model selection procedure (i.e., required the smallest sample size). The advocated model selection procedure is illustrated with norming data of an intelligence test.

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25

Chang, Chih-Hao, Hsin-Cheng Huang, and Ching-Kang Ing. "Asymptotic theory of generalized information criterion for geostatistical regression model selection." Annals of Statistics 42, no. 6 (December 2014): 2441–68. http://dx.doi.org/10.1214/14-aos1258.

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26

Liu, Kuang-Hung, and Clément Kostov. "Multimodel adaptive subtraction with regularized parameter selection via generalized information criterion." GEOPHYSICS 80, no. 3 (May 2015): V33—V45. http://dx.doi.org/10.1190/geo2014-0474.1.

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27

Park, Heewon, and Sadanori Konishi. "Principal component selection via adaptive regularization method and generalized information criterion." Statistical Papers 58, no. 1 (July 7, 2015): 147–60. http://dx.doi.org/10.1007/s00362-015-0691-1.

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28

Carvalho, Fábio Janoni, Denise Garcia de Santana, and Lúcio Borges de Araújo. "Why analyze germination experiments using Generalized Linear Models?" Journal of Seed Science 40, no. 3 (September 2018): 281–87. http://dx.doi.org/10.1590/2317-1545v40n3185259.

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Abstract: We compared the goodness of fit and efficiency of models for germination. Generalized Linear Models (GLMs) were performed with a randomized component corresponding to the percentage of germination for a normal distribution or to the number of germinated seeds for a binomial distribution. Lower levels of Akaikes’s Information Criterion (AIC) and Bayesian Information Criterion (BIC) combined, data adherence to simulated envelopes of normal plots and corrected confidence intervals for the means guaranteed the binomial model a better fit, justifying the importance of GLMs with binomial distribution. Some authors criticize the inappropriate use of analysis of variance (ANOVA) for discrete data such as copaiba oil, but we noted that all model assumptions were met, even though the species had dormant seeds with irregular germination.

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29

Kuiper, R. M., H. Hoijtink, and M. J. Silvapulle. "An Akaike-type information criterion for model selection under inequality constraints." Biometrika 98, no. 2 (April 22, 2011): 495–501. http://dx.doi.org/10.1093/biomet/asr002.

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Abstract The Akaike information criterion for model selection presupposes that the parameter space is not subject to order restrictions or inequality constraints. Anraku (1999) proposed a modified version of this criterion, called the order-restricted information criterion, for model selection in the one-way analysis of variance model when the population means are monotonic. We propose a generalization of this to the case when the population means may be restricted by a mixture of linear equality and inequality constraints. If the model has no inequality constraints, then the generalized order-restricted information criterion coincides with the Akaike information criterion. Thus, the former extends the applicability of the latter to model selection in multi-way analysis of variance models when some models may have inequality constraints while others may not. Simulation shows that the information criterion proposed in this paper performs well in selecting the correct model.

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30

Alahmadi, F., N. A. Rahman, and M. Abdulrazzak. "Evaluation of the best fit distribution for partial duration series of daily rainfall in Madinah, western Saudi Arabia." Proceedings of the International Association of Hydrological Sciences 364 (September 16, 2014): 159–63. http://dx.doi.org/10.5194/piahs-364-159-2014.

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Abstract. Rainfall frequency analysis is an essential tool for the design of water related infrastructure. It can be used to predict future flood magnitudes for a given magnitude and frequency of extreme rainfall events. This study analyses the application of rainfall partial duration series (PDS) in the vast growing urban Madinah city located in the western part of Saudi Arabia. Different statistical distributions were applied (i.e. Normal, Log Normal, Extreme Value type I, Generalized Extreme Value, Pearson Type III, Log Pearson Type III) and their distribution parameters were estimated using L-moments methods. Also, different selection criteria models are applied, e.g. Akaike Information Criterion (AIC), Corrected Akaike Information Criterion (AICc), Bayesian Information Criterion (BIC) and Anderson-Darling Criterion (ADC). The analysis indicated the advantage of Generalized Extreme Value as the best fit statistical distribution for Madinah partial duration daily rainfall series. The outcome of such an evaluation can contribute toward better design criteria for flood management, especially flood protection measures.

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31

Zhang, Xinyu, and Hua Liang. "Focused information criterion and model averaging for generalized additive partial linear models." Annals of Statistics 39, no. 1 (February 2011): 174–200. http://dx.doi.org/10.1214/10-aos832.

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32

Żak-Szatkowska, Małgorzata, and Małgorzata Bogdan. "Modified versions of the Bayesian Information Criterion for sparse Generalized Linear Models." Computational Statistics & Data Analysis 55, no. 11 (November 2011): 2908–24. http://dx.doi.org/10.1016/j.csda.2011.04.016.

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33

Bhansali, R. J. "A derivation of the information criteria for selecting autoregressive models." Advances in Applied Probability 18, no. 2 (June 1986): 360–87. http://dx.doi.org/10.2307/1427304.

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The Akaike information criterion, AIC, for autoregressive model selection is derived by adopting −2T times the expected predictive density of a future observation of an independent process as a loss function, where T is the length of the observed time series. The conditions under which AIC provides an asymptotically unbiased estimator of the corresponding risk function are derived. When the unbiasedness property fails, the use of AIC is justified heuristically. However, a method for estimating the risk function, which is applicable for all fitted orders, is given. A derivation of the generalized information criterion, AICα, is also given; the loss function used being obtained by a modification of the Kullback-Leibler information measure. Results paralleling those for AIC are also obtained for the AICα criterion.

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34

Kamaruzaman, Izzat Fakhruddin, Wan Zawiah Wan Zin, and Noratiqah Mohd Ariff. "A generalized bivariate copula for flood analysis in Peninsular Malaysia." Malaysian Journal of Fundamental and Applied Sciences 15, no. 1 (February 4, 2019): 38–49. http://dx.doi.org/10.11113/mjfas.v15n2019.1275.

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This study generalized the best copula to characterize the joint probability distribution between rainfall severity and duration in Peninsular Malaysia using two dimensional copulas. Specifically, to construct copulas, Inference Function for Margins (IFM) and Canonical Maximum Likelihood (CML) methods were specially exploited. For the purpose of achieving copula fitting, the derived rainfall variables by making use of the Standardized Precipitation Index (SPI) were fitted into several distributions. Five copulas, namely Gaussian, Clayton, Frank, Joe and Gumbel were put to the tests to establish the best data fitted copula. The tests produced acknowledged and satisfactory results of copula fitting for rainfall severity and duration. Surveying the Akaike Information Criterion (AIC) and the Bayesian Information Criterion (BIC), only three copulas produced a better fit for parametric and semi parametric approaches. Finally, two consistency tests were conducted and the results shown that Frank Copula produced consistent results.

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35

Kilai, Mutua, Gichuhi A. Waititu, Wanjoya A. Kibira, Ramy Aldallal, M. E. Bakr, Yusra A. Tashkandy, and Fathy H. Riad. "Information Approach for Change Point Analysis of EGGAPE Distribution and Application to COVID-19 Data." Mathematical Problems in Engineering 2022 (September 27, 2022): 1–11. http://dx.doi.org/10.1155/2022/9924902.

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The exponentiated generalized Gull alpha power exponential distribution is an extension of the exponential distribution that can model data characterized by various shapes of the hazard function. However, change point problem has not been studied for this distribution. In this study, the change point detection of the parameters of the exponentiated generalized Gull alpha power exponential distribution is studied using the modified information criterion. In addition, the binary segmentation procedure is used to identify multiple change point locations. The assumption is that all the parameters of the EGGAPE distributions are considered changeable. Simulation study is conducted to illustrate the power of the modified information criterion in detecting change point in the parameters with different sample sizes. Three applications related to COVID-19 data are used to demonstrate the applicability of the MIC in detecting change point in real life scenario.

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36

Ahsanullah, M., M. Shakil, B. M. Golam Kibria, and M. Elgarhy. "On a Generalized Burr Life-Testing Model: Characterization, Reliability, Simulation, and Akaike Information Criterion." Journal of Statistical Theory and Applications 18, no. 3 (2019): 259. http://dx.doi.org/10.2991/jsta.d.190818.001.

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37

Bhansali, R. J. "A derivation of the information criteria for selecting autoregressive models." Advances in Applied Probability 18, no. 02 (June 1986): 360–87. http://dx.doi.org/10.1017/s0001867800015809.

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The Akaike information criterion, AIC, for autoregressive model selection is derived by adopting −2Ttimes the expected predictive density of a future observation of an independent process as a loss function, whereTis the length of the observed time series. The conditions under which AIC provides an asymptotically unbiased estimator of the corresponding risk function are derived. When the unbiasedness property fails, the use of AIC is justified heuristically. However, a method for estimating the risk function, which is applicable for all fitted orders, is given. A derivation of the generalized information criterion, AICα, is also given; the loss function used being obtained by a modification of the Kullback-Leibler information measure. Results paralleling those for AIC are also obtained for the AICαcriterion.

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38

Hamad, Dr Abed Ali, and Dr Ahmad Hussein Battal. "Use GARCH Models to Build a Econometric Model to Predict Average Daily Closing Prices of the Iraqi Stock Exchange for the Period 2013-2016." Webology 18, Special Issue 04 (September 30, 2021): 385–400. http://dx.doi.org/10.14704/web/v18si04/web18136.

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This research aims to build a standard model for the analysis and prediction of the average daily closing price fluctuations for companies registered in the Iraq Stock Exchange for the period 07/01/2013 to 30/06/2016, using the conditional generalized Heteroscedasticity Generalized Autoregressive (GARCH) models. As these models deal with the fluctuations that occur in the financial time series. The results of the analysis showed that the best model for predicting the volatility of average closing prices in the Iraq Stock Exchange is the EGARCH model (3,1), depending on the statistical criteria used in the preference between the models (Akaike Information Criterion, Schwarz Criterion), and these models can provide information for investors in order to reduce the risk resulting from fluctuations in stock prices in the Iraqi financial market.

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39

Ramirez, Ana I., and Prianka N. Seneviratne. "Transit Route Design Applications Using Geographic Information Systems." Transportation Research Record: Journal of the Transportation Research Board 1557, no. 1 (January 1996): 10–14. http://dx.doi.org/10.1177/0361198196155700102.

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The design of public transportation networks is made difficult by a multitude of conflicting objectives. For example, networks designed to minimize travel time cannot be expected to maximize coverage and accessibility. Two simple methods for designing and improving public transportation routes with the aid of TransCAD, a geographic information system, are presented. The first method involves a direct demand model that uses readily available socioeconomic and demographic variables to compute a generalized impedance function, which subsequently is used to determine the best alignment for transit routes. The second uses rider origin and destination data (addresses) and TransCAD's address-matching capabilities to improve transit coverage to specific heavy-traffic locations such as universities and hospitals. The first method is illustrated using data from Logan, Utah—a newly urbanized community with a population of approximately 33,000 people and home to Utah State University, which, beginning in 1994, has had an annual enrollment of more than 16,000 students. Two alignments were determined for the same origin-destination pair. The first alignment represents the route established with the sole criterion of minimizing travel distance, and the second represents the route established with the criterion of minimizing the generalized impedance function. When the alignments are compared, the second is found to increase route coverage significantly, but at the expense of a slight increase in travel time.

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40

Zhao, Ji, Yuzong Mu, Yanping Qiao, and Qiang Li. "Newton Recursion Based Random Data-Reusing Generalized Maximum Correntropy Criterion Adaptive Filtering Algorithm." Entropy 24, no. 12 (December 18, 2022): 1845. http://dx.doi.org/10.3390/e24121845.

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For system identification under impulsive-noise environments, the gradient-based generalized maximum correntropy criterion (GB-GMCC) algorithm can achieve a desirable filtering performance. However, the gradient method only uses the information of the first-order derivative, and the corresponding stagnation point of the method can be a maximum point, a minimum point or a saddle point, and thus the gradient method may not always be a good selection. Furthermore, GB-GMCC merely uses the current input signal to update the weight vector; facing the highly correlated input signal, the convergence rate of GB-GMCC will be dramatically damaged. To overcome these problems, based on the Newton recursion method and the data-reusing method, this paper proposes a robust adaptive filtering algorithm, which is called the Newton recursion-based data-reusing GMCC (NR-DR-GMCC). On the one hand, based on the Newton recursion method, NR-DR-GMCC can use the information of the second-order derivative to update the weight vector. On the other hand, by using the data-reusing method, our proposal uses the information of the latest M input vectors to improve the convergence performance of GB-GMCC. In addition, to further enhance the filtering performance of NR-DR-GMCC, a random strategy can be used to extract more information from the past M input vectors, and thus we obtain an enhanced NR-DR-GMCC algorithm, which is called the Newton recursion-based random data-reusing GMCC (NR-RDR-GMCC) algorithm. Compared with existing algorithms, simulation results under system identification and acoustic echo cancellation are conducted and validate that NR-RDR-GMCC can provide a better filtering performance in terms of filtering accuracy and convergence rate.

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41

Shahraki, N., S. Marofi, and S. Ghazanfari. "Improving daily rainfall extremes simulation using the generalized Pareto distribution: a case study in Western Iran." Climate Research 85 (January 13, 2022): 193–204. http://dx.doi.org/10.3354/cr01665.

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Prediction of the occurrence or non-occurrence of daily rainfall plays a significant role in agricultural planning and water resource management projects. In this study, gamma distribution function (GDF), kernel, and exponential (EXP) distributions were coupled (piecewise) with a generalized Pareto distribution. Thus, the gamma-generalized Pareto (GGP), kernel-generalized Pareto (KGP), and exponential-generalized Pareto (EGP) models were used. The aim of the present study was to introduce new methods to modify the simulated generation of extreme rainfall amounts of rainy seasons based on the preserved spatial correlation. The best approach was identified using the normalized root mean square error (NRMSE) criterion. For this purpose, the 30-yr daily rainfall datasets of 21 synoptic weather stations located in different climates of West Iran were analyzed. The first, second, and third-order Markov chain (MC) models were used to describe rainfall time series frequencies. The best MC model order was detected using the Akaike information criterion and Bayesian information criterion. Based on the best identified MC model order, the best piecewise distribution models, and the Wilks approach, rainfall events were modeled with regard to the spatial correlation among the study stations. The performance of the Wilks approach was verified using the coefficient of determination. The daily rainfall simulation resulted in a good agreement between the observed and the generated rainfall data. Hence, the proposed approach is capable of helping water resource managers in different contexts of agricultural planning.

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42

Setiawan, E., N. Herawati, and K. Nisa. "Modeling Stock Return Data using Asymmetric Volatility Models : A Performance Comparison based on the Akaike Information Criterion and Schwarz Criterion." Journal of Engineering and Scientific Research 1, no. 1 (June 1, 2019): 40. http://dx.doi.org/10.23960/jesr.v1i1.9.

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The Generalized Autoregressive Conditional Heteroscedasticity (GARCH) modelhas been widely used in time series forecasting especially with asymmetricvolatility data. As the generalization of autoregressive conditionalheteroscedasticity model, GARCH is known to be more flexible to lag structures.Some enhancements of GARCH models were introduced in literatures, among themare Exponential GARCH (EGARCH), Threshold GARCH (TGARCH) andAsymmetric Power GARCH (APGARCH) models. This paper aims to compare theperformance of the three enhancements of the asymmetric volatility models bymeans of applying the three models to estimate real daily stock return volatilitydata. The presence of leverage effects in empirical series is investigated. Based onthe value of Akaike information and Schwarz criterions, the result showed that thebest forecasting model for daily stock return data is the APARCH model.Keywords: Volatility, GARCH, TGARCH, EGARCH, APARCH, AIC and SC.

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43

Dong, Tuochuan, Kristopher Attwood, Alan Hutson, Song Liu, and Lili Tian. "A new diagnostic accuracy measure and cut-point selection criterion." Statistical Methods in Medical Research 26, no. 6 (October 20, 2015): 2832–52. http://dx.doi.org/10.1177/0962280215611631.

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Most diagnostic accuracy measures and criteria for selecting optimal cut-points are only applicable to diseases with binary or three stages. Currently, there exist two diagnostic measures for diseases with general k stages: the hypervolume under the manifold and the generalized Youden index. While hypervolume under the manifold cannot be used for cut-points selection, generalized Youden index is only defined upon correct classification rates. This paper proposes a new measure named maximum absolute determinant for diseases with k stages ([Formula: see text]). This comprehensive new measure utilizes all the available classification information and serves as a cut-points selection criterion as well. Both the geometric and probabilistic interpretations for the new measure are examined. Power and simulation studies are carried out to investigate its performance as a measure of diagnostic accuracy as well as cut-points selection criterion. A real data set from Alzheimer’s Disease Neuroimaging Initiative is analyzed using the proposed maximum absolute determinant.

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Joo, Seang-Hwane, Philseok Lee, and Stephen Stark. "Bayesian Approaches for Detecting Differential Item Functioning Using the Generalized Graded Unfolding Model." Applied Psychological Measurement 46, no. 2 (February 10, 2022): 98–115. http://dx.doi.org/10.1177/01466216211066606.

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Differential item functioning (DIF) analysis is one of the most important applications of item response theory (IRT) in psychological assessment. This study examined the performance of two Bayesian DIF methods, Bayes factor (BF) and deviance information criterion (DIC), with the generalized graded unfolding model (GGUM). The Type I error and power were investigated in a Monte Carlo simulation that manipulated sample size, DIF source, DIF size, DIF location, subpopulation trait distribution, and type of baseline model. We also examined the performance of two likelihood-based methods, the likelihood ratio (LR) test and Akaike information criterion (AIC), using marginal maximum likelihood (MML) estimation for comparison with past DIF research. The results indicated that the proposed BF and DIC methods provided well-controlled Type I error and high power using a free-baseline model implementation, their performance was superior to LR and AIC in terms of Type I error rates when the reference and focal group trait distributions differed. The implications and recommendations for applied research are discussed.

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Setiawan, Eri, Netti Herawati, and Khoirin Nisa. "Modeling Stock Return Data Using Asymmetric Volatility Models: A Performance Comparison Based On the Akaike Information Criterion and Schwarz Criterion." INSIST 3, no. 2 (October 20, 2018): 160. http://dx.doi.org/10.23960/ins.v3i2.160.

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The Generalized Autoregressive Conditional Heteroscedasticity (GARCH) model has been widely used in time series forecasting especially with asymmetric volatility data. As the generalization of autoregressive conditional heteroscedasticity model, GARCH is known to be more flexible to lag structures. Some enhancements of GARCH models were introduced in literatures, among them are Exponential GARCH (EGARCH), Threshold GARCH (TGARCH) and Asymmetric Power GARCH (APGARCH) models. This paper aims to compare the performance of the three enhancements of the asymmetric volatility models by means of applying the three models to estimate real daily stock return volatility data. The presence of leverage effects in empirical series is investigated. Based on the value of Akaike information and Schwarz criterions, the result showed that the best forecasting model for our daily stock return data is the APARCH model.

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Thupeng, Wilson Moseki. "Modelling the Botswana Pula/Us Dollar exchange rate using the Skewed generalized t (SGT) distributions." International Journal of Scientific Research and Management 9, no. 12 (December 21, 2021): 10–16. http://dx.doi.org/10.18535/ijsrm/v9i12.as1.

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The economy of Botswana heavily relies on mineral exports (mainly diamond exports), which are largely dependent on the exchange rate. And, the US Dollar is one of the most important currencies in the basket of currencies to which the Botswana Pula is pegged. Therefore, this paper seeks to empirically establish the baseline characteristics of the Botswana Pula (BWP) and the US Dollar (USD) exchange rate and to identify the most plausible probability distribution from the skewed generalized t (SGT) family that can be used to model the log-returns of the daily BWP/USD exchange rates for the period January 2001 to December 2020. The SGT family is a highly versatile class of models that can capture the skewness and kleptokurticity that are inherent in financial time series. Four probability distributions are considered in this study: skewed t, skewed generalized error, generalized t and skewed generalized t. The maximum likelihood approach is used to estimate the parameters of each model. Model comparison and selection are based on the Akaike information criterion (AIC) and Bayesian information criterion (BIC). The results of the study show that the daily BWP/USD exchange rate series is nonnormal, negatively skewed heavy-tailed. It is also found that, based on the values of both the AIC and BIC, the model that gives the best fit to the data is the skewed t, which is closely followed by the skewed generalized error distribution, while the generalized t gives the worst fit. Keywords: Pula/US Dollar exchange rate, log returns, Generalized t distribution, Skewed generalized error distribution, Skewed generalized t distribution, Skewed t distribution, skewness, kurtosis, maximum likelihood

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Kang, Seunghoon, Woochul Lim, Su-Gil Cho, Sanghyun Park, Minuk Lee, Jong-Su Choi, Sup Hong, and Tae Hee Lee. "Threshold Estimation of Generalized Pareto Distribution Based on Akaike Information Criterion for Accurate Reliability Analysis." Transactions of the Korean Society of Mechanical Engineers A 39, no. 2 (February 1, 2015): 163–68. http://dx.doi.org/10.3795/ksme-a.2015.39.2.163.

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Ratnasingama, Suthakaran, Elena Buzaianub, and Wei Ning. "Modified information criterion for testing changes in generalized lambda distribution model based on confidence distribution." Communications for Statistical Applications and Methods 29, no. 3 (May 31, 2022): 301–17. http://dx.doi.org/10.29220/csam.2022.29.3.301.

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Sulistiowati, Dwi, Maya Sari Syahrul, and Iswan Rina. "Pemodelan Harga Saham Menggunakan Arma-Garch." Jurnal Penelitian Dan Pengkajian Ilmiah Eksakta 1, no. 2 (July 19, 2022): 89–93. http://dx.doi.org/10.47233/jppie.v1i2.532.

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Autoregressive Conditional Heteroscedasticity (ARCH) and Generalized Autoregressive Conditional Heteroskedasticity (GARCH) models were used for modeling with heteroscedasticity data. This study aims to determine the time series model on the stock price data of PT Triputra Agro Persada Tbk. (TAPG) with modeling ARMA, ARCH and GARCH. Based on the smallest Akaike Information Criterion (AIC) and Schwarz Criterion (SC), it shows that the ARMA(1,0)-GARCH(2,1) model is the best model for predicting the value of TAPG stock prices.

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Huang, Chun-Kai, Knowledge Chinhamu, Chun-Sung Huang, and Jahvaid Hammujuddy. "Generalized Hyperbolic Distributions And Value-At-Risk Estimation For The South African Mining Index." International Business & Economics Research Journal (IBER) 13, no. 2 (February 27, 2014): 319. http://dx.doi.org/10.19030/iber.v13i2.8447.

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South Africa is a cornucopia of mineral riches and the performance of its mining industry has significant impacts on the economy. Hence, an accurate distributional assumption of the underlying mining index returns is imperative for the forecasting and understanding of the financial market. In this paper, we propose three subclasses of the generalized hyperbolic distributions as appropriate models for the Johannesburg Stock Exchange (JSE) Mining Index returns. These models are shown to outperform the traditional assumption of normality and accommodate for a number of stylized features, such as excess kurtosis and volatility clustering, embedded within the financial data. The models are compared using the Akaike Information Criterion (AIC), the Bayesian Information Criterion (BIC) and log-likelihoods. In addition, Value-at-Risk (VaR) estimation and backtesting were also performed to test the extreme tails. The various criteria utilized suggest the generalized hyperbolic (GH) skew Students t-distribution as the most robust model for the South African Mining Index returns.

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