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

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

Research output: Contribution to journalArticle

2 Citations (Scopus)

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.

Original languageEnglish (US)
Pages (from-to)4190-4203
Number of pages14
JournalComputational Statistics and Data Analysis
Volume56
Issue number12
DOIs
StatePublished - Dec 1 2012

Fingerprint

Structural Equation Model
Random Coefficients
Longitudinal Data
Bayesian Approach
Adjacent
Coregionalization
Metropolis-Hastings Algorithm
Gibbs Sampler
Structural Parameters
Multivariate Data
Latent Variables
Spatial Data
Unknown Parameters
Simulation Study
Vary
Methodology
Modeling
Estimate
Emotion

All Science Journal Classification (ASJC) codes

  • Computational Mathematics
  • Computational Theory and Mathematics
  • Statistics and Probability
  • Applied Mathematics

Cite this

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A Bayesian approach for generalized random coefficient structural equation models for longitudinal data with adjacent time effects. / Song, Xin Yuan; Tang, Nian Sheng; Chow, Sy-Miin.

In: Computational Statistics and Data Analysis, Vol. 56, No. 12, 01.12.2012, p. 4190-4203.

Research output: Contribution to journalArticle

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