title: Bayesian semiparametric analysis of structural equation models with mixed continuous and unordered categorical variables.
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.
authors.affiliation: Department of Statistics, The Chinese University of Hong Kong, Shatin, N.T., Hong Kong. xysong@sta.cuhk.edu.hk
authors.forename: Xin-Yuan
authors.initials: XY
authors.lastname: Song
authors.forename: Ye-Mao
authors.initials: YM
authors.lastname: Xia
authors.forename: Sik-Yum
authors.initials: SY
authors.lastname: Lee
mesh_terms: D001499:Bayes Theorem; D001699:Biometry; D003928:Diabetic Nephropathies / Q000235:genetics / Q000503:physiopathology; D005838:Genotype; D006801:Humans; D016013:Likelihood Functions; D015233:Models, Statistical; D010641:Phenotype