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 \CG{to handle} missing and heavy-\CG{tailed} data‎. ‎In this model‎, ‎the distribution of factors loading and errors arise from the multivariate normal mean-variance mixture of‎ \CG{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.