Interpretation and inference in mixture models: Simple MCMC works
Computational Statistics & Data Analysis
Auxiliary mixture sampling with applications to logistic models
Computational Statistics & Data Analysis
A Dirichlet process mixture model for the analysis of correlated binary responses
Computational Statistics & Data Analysis
Bayesian binary kernel probit model for microarray based cancer classification and gene selection
Computational Statistics & Data Analysis
Bayesian model choice based on Monte Carlo estimates of posterior model probabilities
Computational Statistics & Data Analysis
Bayesian estimation of random effects models for multivariate responses of mixed data
Computational Statistics & Data Analysis
| Hi-index | 0.03 |
Considering latent heterogeneity is of special importance in nonlinear models in order to gauge correctly the effect of explanatory variables on the dependent variable. A stratified model-based clustering approach is adapted for modeling latent heterogeneity in binary panel probit models. Within a Bayesian framework an estimation algorithm dealing with the inherent label switching problem is provided. Determination of the number of clusters is based on the marginal likelihood and a cross-validation approach. A simulation study is conducted to assess the ability of both approaches to determine on the correct number of clusters indicating high accuracy for the marginal likelihood criterion, with the cross-validation approach performing similarly well in most circumstances. Different concepts of marginal effects incorporating latent heterogeneity at different degrees arise within the considered model setup and are directly at hand within Bayesian estimation via MCMC methodology. An empirical illustration of the methodology developed indicates that consideration of latent heterogeneity via latent clusters provides the preferred model specification over a pooled and a random coefficient specification.