Flexible Parsimonious Mixture of Skew Factor Analysis Based on Normal Mean--Variance Birnbaum-Saunders
Year: 2024
Type: article
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.
Authors: Farzane Hashemi, Jalal Askari, Saeed Darijani
Institutions: University of Kashan, Farhangian University
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W6963771820
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