Bayesian analysis of correlated misclassified binary data

被引：30

作者：

Paulino, CD

Silva, G

Achcar, JA

机构：

[1] Univ Tecn Lisboa, Inst Super Tecn, Dept Matemat, P-1049001 Lisbon, Portugal

[2] Univ Tecn Lisboa, Inst Super Tecn, Ctr Matemat & Aplicacoes, P-1049001 Lisbon, Portugal

[3] Univ Sao Paulo, Fac Med Ribeirao Preto, Sao Paulo, Brazil

来源：

COMPUTATIONAL STATISTICS & DATA ANALYSIS | 2005年 / 49卷 / 04期

基金：

巴西圣保罗研究基金会;

关键词：

binary regression model; misclassification; random effects; Bayesian inference; Markov chain; Monte Carlo methods;

D O I：

10.1016/j.csda.2004.07.004

中图分类号：

TP39 [计算机的应用];

学科分类号：

081203 ; 0835 ;

摘要：

A Bayesian analysis for a random effect binary logistic regression model in the presence of misclassified data is considered. The introduction of a random effect captures the possible correlation among the binary data in each covariate pattern and hence may provide a good alternative to standard models in terms of overall fit. Markov Chain Monte Carlo methods are applied to perform the computations needed to draw inferences and make model assessment, through an illustrative example involving a real medical data set. (c) 2004 Elsevier B.V. All rights reserved.

引用

页码：1120 / 1131

页数：12

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