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.