Latent variable models and factors analysis
Latent variable models and factors analysis
Some representations of the multivariate Bernoulli and binomial distributions
Journal of Multivariate Analysis
Accuracy of Laplace approximation for discrete response mixed models
Computational Statistics & Data Analysis
Efficient direct sampling MCEM algorithm for latent variable models with binary responses
Computational Statistics & Data Analysis
Application of H-likelihood to factor analysis models with binary response data
Journal of Multivariate Analysis
| Hi-index | 0.03 |
Based on the Bayes modal estimate of factor scores in binary latent variable models, this paper proposes two new limited information estimators for the factor analysis model with a logistic link function for binary data based on Bernoulli distributions up to the second and the third order with maximum likelihood estimation and Laplace approximations to required integrals. These estimators and two existing limited information weighted least squares estimators are studied empirically. The limited information estimators compare favorably to full information estimators based on marginal maximum likelihood, MCMC, and multinomial distribution with a Laplace approximation methodology. Among the various estimators, Maydeu-Olivares and Joe's (2005) weighted least squares limited information estimators implemented with Laplace approximations for probabilities are shown in a simulation to have the best root mean square errors.