BAYESIAN INFERENCE FOR FINITE MIXTURES IN CONFIRMATORY FACTOR ANALYSIS

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概要

• 論文の詳細を見る

• The aim of this paper is to apply Bayesian methods via the Gibbs sampler to multivariate normal mixtures whose means and covariance matrices are structured as confirmatory factor analysis models. This estimation method uses the Gibbs sampling, and does not rely on the asymptotic theory nor on any other "sophisticated" MCMC methods. And yet, it can handle easily the cases where common parameterization between components is assumed and/or some parameters are linearly constrained(e.g., they are equal), which was impossible in previous studies. A simulation study showed that the proposed method is effective even for data in which the degree of separation is so small that the asymptotic theory could not apply. It is also shown that the proposed method applied to real data produced results capable of meaningful interpretation.

• 日本行動計量学会の論文

著者

• Hoshino Takahiro

  Department Of Congnitive And Behavioral Science University Of Tokyo

• Hoshino Takahiro

  Department Of Cognitive And Behavioral Science The University Of Tokyo

関連論文

• BAYESIAN PROCRUSTES SOLUTION
• BAYESIAN INFERENCE FOR FINITE MIXTURES IN CONFIRMATORY FACTOR ANALYSIS
• A LATENT VARIABLE MODEL WITH NON-IGNORABLE MISSING DATA

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