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Original Articles

Model selection criteria in beta regression with varying dispersion

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Pages 729-746 | Received 15 May 2014, Accepted 10 Oct 2014, Published online: 21 Oct 2016

References

  • Akaike, H. (1973). Information theory and an extension of the maximum likelihood principle. In: Petrov N., Csaki F., eds.Proceeding of the 2nd International Symposium on Information Theory. Budapest: Akademiai Kiado, pp. 267–281.
  • Akaike, H. (1974). A new look at the statistical model identification. IEEE Transactions on Automatic Control 19(6):716–723.
  • Akaike, H. (1978). A Bayesian analysis of the minimum AIC procedure. Annals of the Institute of Statistical Mathematics 30(1):9–14.
  • Bengtsson, T., Cavanaugh, J. (2006). An improved Akaike information criterion for state-space model selection. Computational Statistics & Data Analysis 50(10):2635–2654.
  • Brehm, J., Gates, S. (1993). Donut shops and speed traps: Evaluating models of supervision on police behavior. American Journal of Political Science 37(2):555–581.
  • Caby, E. (2000). Review: [regression and time series model selection]. Technometrics 42(2):214–216.
  • Cribari-Neto, F., Queiroz, M. P. (2014). On testing inference in beta regressions. Journal of Statistical Computation and Simulation 84(1):186–203.
  • Cribari-Neto, F., Souza, T. (2012). Testing inference in variable dispersion beta regressions. Journal of Statistical Computational and Simulation 82(12):1827–1843.
  • Espinheira, P. L. (2007). Regressão beta. Ph.D. dissertation, São Paulo, Brazil: Universidade de São Paulo (USP).
  • Ferrari, S. L. P., Cribari-Neto, F. (2004). Beta regression for modelling rates and proportions. Journal of Applied Statistics 31(7):799–815.
  • Ferrari, S. L. P., Espinheira, P. L., Cribari-Neto, F. (2011). Diagnostic tools in beta regression with varying dispersion. Statistica Neerlandica 65(3):337–351.
  • Frazer, L. N., Genz, A. S., Fletcher, C. H. (2009). Toward parsimony in shoreline change prediction (i): Basis function methods. Journal of Coastal Research 25(2):366–379.
  • Hallgren, R. C., Pierce, S. J., Prokop, L. L., Rowan, J. J., Lee, A. S. (2013). Electromyographic activity of rectus capitis posterior minor muscles associated with voluntary retraction of the head. The Spine Journal, In Press(0), Available at: http://www.sciencedirect.com/science/article/pii/S15299430130 06864.
  • Hannan, E. J., Quinn, B. G. (1979). The determination of the order of an autoregression. Journal of the Royal Statistical Society: Series B 41(2):190–195.
  • Hu, B., Shao, J. (2008). Generalized linear model selection using . Journal of Statistical Planning and Inference 138(12):3705–3712.
  • Hurvich, C. M., Tsai, C. L. (1989). Regression and time series model selection in small samples. Biometrika 76(2):297–307.
  • Kieschnick, R., McCullough, B. D. (2003). Regression analysis of variates observed on (0, 1): Percentages, proportions, and fractions. Statistical Modelling 3(3):193–213.
  • Kullback, S., Leibler, R. A. (1951). On information and sufficiency. The Annals of Mathematical Statistics 22(1):79–86.
  • Liang, H., Zou, G. (2008). Improved AIC selection strategy for survival analysis. Computational Statistics & Data Analysis 52:2538–2548.
  • Long, J. S. (1997). Regression models for categorical and limited dependent variables. 2nd ed. Thousand Oaks, California: SAGE Publications.
  • Mallows, C. L. (1973). Some comments on . Technometrics 15:661–675.
  • McCullagh, P., Nelder, J. (1989). Generalized linear models. 2nd ed. New York: Chapman and Hall.
  • McQuarrie, A. (1999). A small-sample correction for the Schwarz SIC model selection criterion. Statistics & Probability Letters 44(1):79–86.
  • McQuarrie, A., Shumway, R., Tsai, C. L. (1997). The model selection criterion AICu. Statistics & Probability Letters 34(3):285–292.
  • McQuarrie, A., Tsai, C. L. (1998). Regression and time series model selection. Edition 1. Singapore: World Scientific.
  • Mittlböck, M., Schemper, M. (2002). Explained variation for logistic regression - small sample adjustments, confidence intervals, and predictive precision. Biometrical Journal 44(3):263–272.
  • Nagelkerke, N. J. D. (1991). A note on a general definition of the coefficient of determination. Biometrika 78(3):691–692.
  • Nelder, J. A., Lee, Y. (1991). Generalized linear models for the analysis of Taguchi-type experiments. Applied Stochastic Models and Data Analysis 7(1):107–120.
  • Pammer, K., Kevan, A. (2004). The contribution of visual sensitivity, phonological processing, and nonverbal IQ to children’s reading. Scientific Studies of Reading 11(1):33–53.
  • Pan, W. (1999). Bootstrapping likelihood for model selection with small samples. Journal of Computational and Graphical Statistics 8(4):687–698.
  • Paulino, C. D. M., Pereira, C. A. d. B. (1994). On identifiability of parametric statistical models. Journal of the Italian Statistical Society 3(1):125–151.
  • Pereira, T. L., Cribari-Neto, F. (2014). Detecting model misspecification in inflated beta regressions. Communications in Statistics---Simulation and Computation 43(3):631–656.
  • R Development Core Team, (2009). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. Available at: http://www.R-project.org.
  • Rothenberg, T. J. (1971). Identification in parametric models. Econometrica 39(3):577–591.
  • Schwarz, G. (1978). Estimating the dimension of a model. The Annals of Statistics 6(2):461–464.
  • Shang, J., Cavanaugh, J. (2008). Bootstrap variants of the Akaike information criterion for mixed model selection. Computational Statistics & Data Analysis 52(4):2004–2021.
  • Shao, J. (1996). Bootstrap model selection. Journal of the American Statistical Association 91(434):655–665.
  • Shibata, R. (1980). Asymptotically efficient selection of the order of the model for estimating parameters of a linear process. The Annals of Statistics 8(1):147–164.
  • Shi, P., Tsai, C. L. (2002). Regression model selection: A residual likelihood approach. Journal of the Royal Statistical Society: Series B (Statistical Methodology) 64(2):237–252.
  • Shou, Y., Smithson, M. (2015). Evaluating predictors of dispersion: A comparison of dominance analysis and bayesian model averaging. Psychometrika 80(1):236–256.
  • Simas, A. B., Barreto-Souza, W., Rocha, A. V. (2010). Improved estimators for a general class of beta regression models. Computational Statistics & Data Analysis 54(2):348–366.
  • Smithson, M., Verkuilen, J. (2006). A better lemon squeezer? Maximum-likelihood regression with beta-distributed dependent variables. Psychological Methods 11(1):54–71.
  • Smyth, G. K. (1989). Generalized linear models with varying dispersion. Journal of the Royal Statistical Society: Series B (Methodological) 51(1):47–60.
  • Smyth, G., Verbyla, A. (1999). Adjusted likelihood methods for modelling dispersion in generalized linear models. Environmetrics 10(6):695–709.
  • Stasinopoulos, D. M., Rigby, R. A. (2007). Generalized additive models for location scale and shape (GAMLSS) in R. Journal of Statistical Software 23:1–46.
  • Sugiura, N. (1978). Further analysts of the data by Akaike’s information criterion and the finite corrections—further analysts of the data by Akaike’s. Communications in Statistics---Theory and Methods 7(1):13–26.
  • Verhaelen, K., Bouwknegt, M., Carratalà, A., Lodder-Verschoor, F., Diez-Valcarce, M., Rodríguez-Lázaro, D., de Roda Husman, A. M., Rutjes, S. A. (2013). Virus transfer proportions between gloved fingertips, soft berries, and lettuce, and associated health risks. International Journal of Food Microbiology 166(3):419–425.
  • Whiteman, A., Young, D. E., He, X., Chen, T. C., Wagenaar, R. C., Stern, C., Schon, K. (2013). Interaction between serum BDNF and aerobic fitness predicts recognition memory in healthy young adults. Behavioural Brain Research, In Press(0), Available at: http://www.sciencedirect.com/science/article/pii/S01664328130 07109.
  • Wu, L., Li, H. (2012). Variable selection for joint mean and dispersion models of the inverse gaussian distribution. Metrika 75:795–808.
  • Zucco, C. (2008). The president’s “new” constituency: Lula and the pragmatic vote in Brazil’s 2006 presidential elections. Journal of Latin American Studies 40(1):29–49.

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