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2016 Assessing Heterogeneity for Factor Analysis Model with Continuous and Ordinal Outcomes
Ye-Mao Xia, Jian-Wei Gou
J. Appl. Math. 2016: 1-12 (2016). DOI: 10.1155/2016/1648462

Abstract

Factor analysis models with continuous and ordinal responses are a useful tool for assessing relations between the latent variables and mixed observed responses. These models have been successfully applied to many different fields, including behavioral, educational, and social-psychological sciences. However, within the Bayesian analysis framework, most developments are constrained within parametric families, of which the particular distributions are specified for the parameters of interest. This leads to difficulty in dealing with outliers and/or distribution deviations. In this paper, we propose a Bayesian semiparametric modeling for factor analysis model with continuous and ordinal variables. A truncated stick-breaking prior is used to model the distributions of the intercept and/or covariance structural parameters. Bayesian posterior analysis is carried out through the simulation-based method. Blocked Gibbs sampler is implemented to draw observations from the complicated posterior. For model selection, the logarithm of pseudomarginal likelihood is developed to compare the competing models. Empirical results are presented to illustrate the application of the methodology.

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Ye-Mao Xia. Jian-Wei Gou. "Assessing Heterogeneity for Factor Analysis Model with Continuous and Ordinal Outcomes." J. Appl. Math. 2016 1 - 12, 2016. https://doi.org/10.1155/2016/1648462

Information

Received: 8 December 2015; Revised: 23 February 2016; Accepted: 2 March 2016; Published: 2016
First available in Project Euclid: 13 April 2016

zbMATH: 07037264
MathSciNet: MR3484234
Digital Object Identifier: 10.1155/2016/1648462

Rights: Copyright © 2016 Hindawi

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