Please use this identifier to cite or link to this item: http://repositorio.unicamp.br/jspui/handle/REPOSIP/242411
Type: Artigo
Title: A Mixed-effect model for positive responses augmented by zeros
Author: Motta, Mariana Rodrigues
Galvis Soto, Diana Milena
Lachos, Victor H.
Vilca, Filidor
Baltar, Valéria Troncoso
Verly Junior, Eliseu
Fisbergd, Regina Mara
Marchionid, Dirce Maria Lobo
Abstract: In this research article, we propose a class of models for positive and zero responses by means of a zero-augmented mixed regression model. Under this class, we are particularly interested in studying positive responses whose distribution accommodates skewness. At the same time, responses can be zero, and therefore, we justify the use of a zero-augmented mixture model. We model the mean of the positive response in a logarithmic scale and the mixture probability in a logit scale, both as a function of fixed and random effects. Moreover, the random effects link the two random components through their joint distribution and incorporate within-subject correlation because of the repeated measurements and between-subject heterogeneity. A Markov chain Monte Carlo algorithm is tailored to obtain Bayesian posterior distributions of the unknown quantities of interest, and Bayesian case-deletion influence diagnostics based on the q-divergence measure is performed. We apply the proposed method to a dataset from a 24hour dietary recall study conducted in the city of SAo Paulo and present a simulation study to evaluate the performance of the proposed methods. Copyright (c) 2015John Wiley & Sons, Ltd.
In this research article, we propose a class of models for positive and zero responses by means of a zero‐augmented mixed regression model. Under this class, we are particularly interested in studying positive responses whose distribution accommodates skewness. At the same time, responses can be zero, and therefore, we justify the use of a zero‐augmented mixture model. We model the mean of the positive response in a logarithmic scale and the mixture probability in a logit scale, both as a function of fixed and random effects. Moreover, the random effects link the two random components through their joint distribution and incorporate within‐subject correlation because of the repeated measurements and between‐subject heterogeneity. A Markov chain Monte Carlo algorithm is tailored to obtain Bayesian posterior distributions of the unknown quantities of interest, and Bayesian case‐deletion influence diagnostics based on the q‐divergence measure is performed. We apply the proposed method to a dataset from a 24hour dietary recall study conducted in the city of São Paulo and present a simulation study to evaluate the performance of the proposed methods.
Subject: Inferência bayesiana
Distribuição (Probabilidades)
Modelos mistos
Métodos MCMC (Estatística)
Country: Reino Unido
Editor: John Wiley & Sons
Citation: A Mixed-effect Model For Positive Responses Augmented By Zeros. Wiley-blackwell, v. 34, p. 1761-1778 MAY-2015.
Rights: fechado
Identifier DOI: 10.1002/sim.6450
Address: https://onlinelibrary.wiley.com/doi/abs/10.1002/sim.6450
Date Issue: 2015
Appears in Collections:IMECC - Artigos e Outros Documentos

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