Bayesian modeling of the dependence in longitudinal data via partial autocorrelations and marginal variances

Y. Wang and M.J. Daniels

Journal of Multivariate Analysis, 2013, vol. 116, issue C, 130-140

Abstract: Many parameters and positive-definiteness are two major obstacles in estimating and modeling a correlation matrix for longitudinal data. In addition, when longitudinal data is incomplete, incorrectly modeling the correlation matrix often results in bias in estimating mean regression parameters. In this paper, we introduce a flexible and parsimonious class of regression models for a covariance matrix parameterized using marginal variances and partial autocorrelations. The partial autocorrelations can freely vary in the interval (−1,1) while maintaining positive definiteness of the correlation matrix so the regression parameters in these models will have no constraints. We propose a class of priors for the regression coefficients and examine the importance of correctly modeling the correlation structure on estimation of longitudinal (mean) trajectories and the performance of the DIC in choosing the correct correlation model via simulations. The regression approach is illustrated on data from a longitudinal clinical trial.

Keywords: Markov chain Monte Carlo; Generalized linear model; Uniform prior; G-prior (search for similar items in EconPapers)
Date: 2013
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Citations: View citations in EconPapers (1)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:jmvana:v:116:y:2013:i:c:p:130-140

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DOI: 10.1016/j.jmva.2012.11.010

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