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Activity Number: 31
Type: Contributed
Date/Time: Sunday, July 29, 2012 : 2:00 PM to 3:50 PM
Sponsor: Biometrics Section
Abstract - #305490
Title: A Multivariate Hidden Markov Model for the Analysis of Correlated Monotone Binary Processes
Author(s): Maria Jose Garcia Zattera*+ and Alejandro Jara and Emmanuel Lesaffre and Guillermo Marshall
Companies: Pontificia Universidad Católica de Chile and Pontificia Universidad Católica de Chile and Katholieke Universiteit Leuven and Pontificia Universidad Católica de Chile
Address: Facultad De Matematicas, Santiago De Chile, , Chile
Keywords: Multivariate binary data ; Misclassification ; Incidence estimation ; Conditionally specified model ; Bayesian analysis ; Logistic regression

Motivated by a longitudinal oral health study, we propose a multivariate binary inhomogeneous Markov model in which unobserved correlated response variables are subject to an unconstrained misclassification process and have a monotone behavior. The multivariate baseline distributions and Markov transition matrices of the unobserved processes are defined as a function of covariates, throughout the specification of compatible full conditional distributions. Distinct misclassification models are discussed. In all cases, the possibility that different examiners were involved in the scoring of the responses of a given subject across time, is taken into account. A full Bayesian implementation of the model is described and its performance is evaluated using simulated data. We provide theoretical and empirical evidence that the parameters can be estimated without any external information about the misclassification parameters.Finally, the analyses of the motivating problem are presented. M.J. Garcia-Zattera's work was supported by FONDECYT 11110033 grant.

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