Bayesian Analysis of Multivariate Longitudinal Ordinal Data Using Multiple Multivariate Probit Models

نویسندگان

چکیده

Multivariate longitudinal ordinal data are often involved in studies with each individual having more than one measure. However, due to complicated correlation structures within and no explicit likelihood functions, analyzing multivariate is quite challenging. In this paper, Markov chain Monte Carlo (MCMC) sampling methods developed analyze by extending probit (MVP) models for univariate multiple (MMVP) data. The identifiable MVP require the covariance matrix of latent normal variables underlying be a matrix, thus Metropolis-Hastings (MH) algorithm usually necessitated, which brings rigorous task develop efficient MCMC methods. contrast models, non-identifiable can constructed circumvent MH sample Gibbs hence improve mixing convergence components. Therefore, both MMVP presented, their corresponding developed. performances these illustrated through simulation an application using from Russia Longitudinal Monitoring Survey-Higher School Economics (RLMS-HSE).

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ژورنال

عنوان ژورنال: American Journal of Theoretical and Applied Statistics

سال: 2023

ISSN: ['2326-9006', '2326-8999']

DOI: https://doi.org/10.11648/j.ajtas.20231201.11