Bootstrap-based model selection criteria for beta regressions

DOI10.1007/s11749-015-0434-6zbMath1329.62347arXiv1405.4525OpenAlexW3103547696WikidataQ59162968 ScholiaQ59162968MaRDI QIDQ905106

Fábio M. Bayer, Francisco Cribari-Neto

Publication date: 14 January 2016

Published in: Test (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1405.4525

zbMATH Keywords

bootstrapmodel selectioncross-validationAICbeta regressionvarying dispersion

Mathematics Subject Classification ID

Linear inference, regression (62J99) Bootstrap, jackknife and other resampling methods (62F40) Measures of information, entropy (94A17) Statistical ranking and selection procedures (62F07)

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The comparison study of the model selection criteria on the Tobit regression model based on the bootstrap sample augmentation mechanisms ⋮ Skewness of maximum likelihood estimators in the varying dispersion beta regression model ⋮ Resampling-based prediction intervals in beta regressions under correct and incorrect model specification ⋮ Bootstrap-based testing inference in beta regressions ⋮ A procedure for variable selection in double generalized linear models ⋮ On nonlinear beta regression residuals ⋮ Prediction intervals in the beta autoregressive moving average model ⋮ Residual and influence analysis to a general class of simplex regression ⋮ Improved testing inferences for beta regressions with parametric mean link function ⋮ Bessel regression model: Robustness to analyze bounded data ⋮ Beta seasonal autoregressive moving average models

Uses Software

• R
• Ox

Cites Work

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