Parsimonious mixture of mean-mixture of normal distributions with missing data
Clustering multivariate data based on mixture distributions is a usual method to characterize groups and label data sets. Mixture models have recently been received considerable attention to accommodate asymmetric and missing data via exploiting skewed and heavy-tailed distributions. In this paper,...
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| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
Shahid Bahonar University of Kerman
2024-08-01
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| Series: | Journal of Mahani Mathematical Research |
| Subjects: | |
| Online Access: | https://jmmrc.uk.ac.ir/article_4229_243b2a9b6fa5a57b8aac42dc15a4fe7f.pdf |
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| Summary: | Clustering multivariate data based on mixture distributions is a usual method to characterize groups and label data sets. Mixture models have recently been received considerable attention to accommodate asymmetric and missing data via exploiting skewed and heavy-tailed distributions. In this paper, a mixture of multivariate mean-mixture of normal distributions is considered for handling missing data. The EM-type algorithms are carried out to determine maximum likelihood of parameters estimations. We analyzed the real data sets and conducted simulation studies to demonstrate the superiority of the proposed methodology. |
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| ISSN: | 2251-7952 2645-4505 |