Bayesian analysis of non-linear structural equation models with non-ignorable missing outcomes from reproductive dispersion models

A Bayesian analysis of mixture structural equation models with non-ignorable missing Data

In behavioral, biomedical, and social-psychological sciences, it is common to encounter latent variables and heterogeneous data. Mixture structural equation models (SEMs) are very useful methods to analyze these kinds of data. Moreover, the presence of missing data, including both missing responses and missing covariates, is an important issue in practical research. However, limited work has be...

A Bayesian analysis of mixture structural equation models with non-ignorable missing responses and covariates.

In behavioral, biomedical, and social-psychological sciences, it is common to encounter latent variables and heterogeneous data. Mixture structural equation models (SEMs) are very useful methods to analyze these kinds of data. Moreover, the presence of missing data, including both missing responses and missing covariates, is an important issue in practical research. However, limited work has be...

Regression Analysis in Longitudinal Studies with Non-ignorable Missing Outcomes

Maximum Likelihood Estimation of Nonlinear Structural Equation Models with Ignorable Missing Data

The existing maximum likelihood theory and its computer software in structural equation modeling are established on the basis of linear relationships among latent variables with fully observed data. However, in social and behavioral sciences, nonlinear relationships among the latent variables are important for establishing more meaningful models and it is very common to encounter missing data. ...

Monte Carlo EM algorithm in logistic linear models involving non-ignorable missing data

Many data sets obtained from surveys or medical trials often include missing observations. Since ignoring the missing information usually cause bias and inefficiency, an algorithm for estimating parameters is proposed based on the likelihood function of which the missing information is taken account. A binomial response and normal exploratory model for the missing data are assumed. We fit the m...