A Bayesian approach for generalized random coefficient structural equation models for longitudinal data with adjacent time effects

Xin-Yuan Song, Nian-Sheng Tang and Sy-Miin Chow

Computational Statistics & Data Analysis, 2012, vol. 56, issue 12, 4190-4203

Abstract: This paper proposes a generalized random coefficient structural equation model for analyzing longitudinal data by incorporating the correlated structure due to adjacent time effects and by allowing structural parameters to vary across individuals. The coregionalization for modeling multivariate spatial data is adopted to formulate the correlated structure between adjacent time points. A Bayesian approach coupled with the Gibbs sampler and the Metropolis–Hastings algorithm is developed to obtain the Bayesian estimates of unknown parameters and latent variables simultaneously. A simulation study and a real example related to an emotion study are presented to illustrate the newly developed methodology.

Keywords: Adjacent time effects; Bayesian approach; Longitudinal data; Random regression coefficients; Structural equation models (search for similar items in EconPapers)
Date: 2012
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Citations: View citations in EconPapers (1)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:csdana:v:56:y:2012:i:12:p:4190-4203

DOI: 10.1016/j.csda.2012.04.016

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