Flexible Parsimonious Mixture of Skew Factor Analysis‎ ‎Based‎ ‎on‎ ‎Normal‎ ‎Mean--Variance Birnbaum-Saunders

The purpose of this paper is to extend the mixture factor analyzers (MFA) model to handle missing and heavy-tailed data. In this model, the distribution of factors loading and errors arise from the multivariate normal mean-variance mixture of the Birnbaum-Saunders (NMVBS) distribution. By using the structures covariance matrix, we introduce parsimonious MFA based on NMVBS distribution. An Expectation Maximization (EM)-type algorithm is developed for parameter estimation. Simulations study and real data sets represent the efficiency and performance of the proposed model.