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

Abstract

‎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.

Keywords

• normal mean-variance distribution‎
• ‎em-type algorithm‎
• ‎factor analysis‎
• ‎heavy-tail‎
• ‎strongly leptokurtic‎