Statistics and Its Interface
Volume 1 (2008)
Bayesian analysis of nonlinear structural equation models with mixed continuous, ordered and unordered categorical, and nonignorable missing data
Pages: 99 – 114
Structural equation models (SEMs) have been widely applied in examing inter-relationships among latent and observed variables in social, psychological, and medical research. Motivated by the fact that correlated discrete variables and missing data are frequently encountered in practical applications, a nonlinear SEM (NSEM) that accommodates covariates, mixed continuous and discrete variables, and nonignorable missing data is proposed. Bayesian methods for estimation and model comparison are discussed. One real-life data set about cardiovascular disease is used to illustrate the methodologies.
latent variables, ordered and unordered categorical data, nonignorable missing data, Bayesian approach