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 \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) distri...
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| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
University of Kashan
2024-12-01
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| Series: | Mathematics Interdisciplinary Research |
| Subjects: | |
| Online Access: | https://mir.kashanu.ac.ir/article_114583_c88b79e69d0b72add6e7b3c494bd06c5.pdf |
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| Summary: | 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. |
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| ISSN: | 2476-4965 |