Flexible Parsimonious Mixture of Skew Factor Analysis Based on Normal Mean--Variance Birnbaum-Saunders
نویسندگان
1 Department of Applied Mathematics, University of Kashan,Kashan, I. R. Iran
2 Department of Statistics, University of Kashan, Kashan, I. R. Iran
3 Farhangian University Of Kerman, Kerman, I. R. Iran
doi
10.22052/mir.2024.254416.1459چکیده
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.
