Please use this identifier to cite or link to this item:
Type: Artigo
Title: Bayesian modeling and prior sensitivity analysis for zero–one augmented beta regression models with an application to psychometric data
Author: Nogarotto, Danilo Covaes
Azevedo, Caio Lucidius Naberezny
Bazán, Jorge Luis
Abstract: The interest on the analysis of the zero–one augmented beta regression (ZOABR) model has been increasing over the last few years. In this work, we developed a Bayesian inference for the ZOABR model, providing some contributions, namely: we explored the use of Jeffreys-rule and independence Jeffreys prior for some of the parameters, performing a sensitivity study of prior choice, comparing the Bayesian estimates with the maximum likelihood ones and measuring the accuracy of the estimates under several scenarios of interest. The results indicate, in a general way, that: the Bayesian approach, under the Jeffreys-rule prior, was as accurate as the ML one. Also, different from other approaches, we use the predictive distribution of the response to implement Bayesian residuals. To further illustrate the advantages of our approach, we conduct an analysis of a real psychometric data set including a Bayesian residual analysis, where it is shown that misleading inference can be obtained when the data is transformed. That is, when the zeros and ones are transformed to suitable values and the usual beta regression model is considered, instead of the ZOABR model. Finally, future developments are discussed
Subject: Modelos de regressão (Estatística)
Country: Brasil
Editor: Associação Brasileira de Estatística
Rights: Fechado
Identifier DOI: 10.1214/18-BJPS423
Date Issue: 2020
Appears in Collections:IMECC - Artigos e Outros Documentos
FT - Artigos e Outros Documentos

Files in This Item:
File Description SizeFormat 
000531034400006.pdf1.06 MBAdobe PDFView/Open

Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.