Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
10327956 | Computational Statistics & Data Analysis | 2005 | 12 Pages |
Abstract
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.
Related Topics
Physical Sciences and Engineering
Computer Science
Computational Theory and Mathematics
Authors
Carlos Daniel Paulino, Giovani Silva, Jorge Alberto Achcar,