Bayesian semiparametric analysis of structural equation models with mixed continuous and unordered categorical variables

Statistics in Medicine
Xin-Yuan SongSik-Yum Lee

Abstract

Recently, structural equation models (SEMs) have been applied for analyzing interrelationships among observed and latent variables in biological and medical research. Latent variables in these models are typically assumed to have a normal distribution. This article considers a Bayesian semparametric SEM with covariates, and mixed continuous and unordered categorical variables, in which the explanatory latent variables in the structural equation are modeled via an appropriate truncated Dirichlet process with a stick-breaking procedure. Results obtained from a simulation study and an analysis of a real medical data set are presented to illustrate the methodology.

References

Jan 1, 1992·Statistical Methods in Medical Research·P M Bentler, J A Stein
Sep 1, 1992·Psychological Bulletin·L T HuY Kano
Jul 9, 1998·The New England Journal of Medicine·P FiorettoM Mauer
Nov 17, 2007·Statistics in Medicine·Sik-Yum LeeXin-Yuan Song

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Citations

Jul 21, 2012·The European Journal of Health Economics : HEPAC : Health Economics in Prevention and Care·Fredrik Odegaard, Pontus Roos
Jul 6, 2016·Statistics in Medicine·Chunfeng Ren, Yongyun Shin

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