Bayesian Mixture Model of Extended Redundancy Analysis

Minjung Kyung, Ju Hyun Park, Ji Yeh Choi

Research output: Contribution to journalArticlepeer-review

1 Scopus citations

Abstract

Extended redundancy analysis (ERA), a generalized version of redundancy analysis (RA), has been proposed as a useful method for examining interrelationships among multiple sets of variables in multivariate linear regression models. As a limitation of the extant RA or ERA analyses, however, parameters are estimated by aggregating data across all observations even in a case where the study population could consist of several heterogeneous subpopulations. In this paper, we propose a Bayesian mixture extension of ERA to obtain both probabilistic classification of observations into a number of subpopulations and estimation of ERA models within each subpopulation. It specifically estimates the posterior probabilities of observations belonging to different subpopulations, subpopulation-specific residual covariance structures, component weights and regression coefficients in a unified manner. We conduct a simulation study to demonstrate the performance of the proposed method in terms of recovering parameters correctly. We also apply the approach to real data to demonstrate its empirical usefulness.

Original languageEnglish
Pages (from-to)946-966
Number of pages21
JournalPsychometrika
Volume87
Issue number3
DOIs
StatePublished - Sep 2022

Keywords

  • Bayesian
  • clustering
  • extended redundancy analysis
  • finite mixture model

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