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

Document Type : Original Scientific Paper

Authors

1 ‎Department of Statistics, ‎University of Kashan, ‎Kashan‎, ‎I‎. ‎R‎. ‎Iran

2 ‎Department of Applied Mathematics, ‎University of Kashan,‎Kashan‎, ‎I‎. ‎R‎. ‎Iran

3 ‎Farhangian University Of Kerman, ‎Kerman‎, ‎I‎. ‎R‎. ‎Iran

10.22052/mir.2024.254416.1459

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

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References

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Mathematics Interdisciplinary Research
Volume 9, Issue 4
In progress
December 2024
Pages 385-411

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