Estimation methods for multivariate Tobit confirmatory factor analysis

Author(s)

Costa, D. R. ; Lachos, V. H. ^[1] ; Bazan, J. L. ^[2] ; Azevedo, C. L. N. ^[1]

Affiliation

^[1] Univ Estadual Campinas, Dept Stat, Campinas, SP - Brazil

^[2] Univ Sao Paulo, Dept Appl Math & Stat, BR-05508 Sao Paulo - Brazil

• Document type: Journal article
• Source: COMPUTATIONAL STATISTICS & DATA ANALYSIS; v. 79, p. 248-260, NOV 2014.
• Web of Science Citations: 4

Abstract

Tobit confirmatory factor analysis is particularly useful in analysis of multivariate data with censored information. Two methods for estimating multivariate Tobit confirmatory factor analysis models with covariates from a Bayesian and likelihood-based perspectives are proposed. In contrast with previous likelihood-based developments that consider Monte Carlo simulations for maximum likelihood estimation, an exact EM-type algorithm is proposed. Also, the estimation of the parameters via MCMC techniques by considering a hierarchical formulation of the model is explored. Bayesian case deletion influence diagnostics based on the q-divergence measure and model selection criteria is also developed and considered in the analysis of a real dataset related to the education assessment field. In addition, a simulation study is conducted to compare the performance of the proposed method with the traditional confirmatory factor analysis. The results show that both methods offer more precise inferences than the traditional confirmatory factor analysis, which ignores the information about the censoring threshold. (C) 2014 Elsevier B.V. All rights reserved. (AU)

• FAPESP's process: 14/02938-9 - Estimation and diagnostics for censored mixed effects models using scale mixtures of skew-normal distributions
• Grantee: Víctor Hugo Lachos Dávila
• Support Opportunities: Regular Research Grants