A Bayesian analysis of finite mixtures in the LISREL model

Hong Tu Zhu; Sik Yum Lee

doi:10.1007/BF02295737

A Bayesian analysis of finite mixtures in the LISREL model

Hong Tu Zhu, Sik Yum Lee

Research output: Contribution to journal › Article › peer-review

53 Scopus citations

Abstract

In this paper, we propose a Bayesian framework for estimating finite mixtures of the LISREL model. The basic idea in our analysis is to augment the observed data of the manifest variables with the latent variables and the allocation variables. The Gibbs sampler is implemented to obtain the Bayesian solution. Other associated statistical inferences, such as the direct estimation of the latent variables, establishment of a goodness-of-fit assessment for a posited model, Bayesian classification, residual and outlier analyses, are discussed. The methodology is illustrated with a simulation study and a real example.

Original language	English (US)
Pages (from-to)	133-152
Number of pages	20
Journal	Psychometrika
Volume	66
Issue number	1
DOIs	https://doi.org/10.1007/BF02295737
State	Published - Mar 2001

Keywords

Bayesian analysis
Bayesian classification
Conditional distributions
Finite mixtures
Gibbs sampler
Goodness-of-ft assessment
LISREL models
Residual and outlier analyses

ASJC Scopus subject areas

General Psychology
Applied Mathematics

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10.1007/BF02295737

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title = "A Bayesian analysis of finite mixtures in the LISREL model",

abstract = "In this paper, we propose a Bayesian framework for estimating finite mixtures of the LISREL model. The basic idea in our analysis is to augment the observed data of the manifest variables with the latent variables and the allocation variables. The Gibbs sampler is implemented to obtain the Bayesian solution. Other associated statistical inferences, such as the direct estimation of the latent variables, establishment of a goodness-of-fit assessment for a posited model, Bayesian classification, residual and outlier analyses, are discussed. The methodology is illustrated with a simulation study and a real example.",

keywords = "Bayesian analysis, Bayesian classification, Conditional distributions, Finite mixtures, Gibbs sampler, Goodness-of-ft assessment, LISREL models, Residual and outlier analyses",

author = "Zhu, {Hong Tu} and Lee, {Sik Yum}",

note = "Funding Information: This research was supported by a Hong Kong UGC Earmarked grant CUHK 4026/97H. The authors are indebted to the Editor, the Associate Editor, and three anonymous reviewers :for constructive comments in improving the paper, and also to ICPSR and the relevant funding agency for allowing the use of the data. The assistance of Michael K.H. Leung and Esther L.S. Tam is gratefully acknowledged.",

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AB - In this paper, we propose a Bayesian framework for estimating finite mixtures of the LISREL model. The basic idea in our analysis is to augment the observed data of the manifest variables with the latent variables and the allocation variables. The Gibbs sampler is implemented to obtain the Bayesian solution. Other associated statistical inferences, such as the direct estimation of the latent variables, establishment of a goodness-of-fit assessment for a posited model, Bayesian classification, residual and outlier analyses, are discussed. The methodology is illustrated with a simulation study and a real example.

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KW - Bayesian classification

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A Bayesian analysis of finite mixtures in the LISREL model

Abstract

Keywords

ASJC Scopus subject areas

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