An EM Gradient Algorithm for Mixture Models with Components Derived from the Manly Transformation
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arXiv
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| Hauptverfasser: | , |
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| Format: | Preprint |
| Veröffentlicht: |
2024
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| _version_ | 1866912513867644928 |
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| author | Clark, Katharine M. McNicholas, Paul D. |
| author_facet | Clark, Katharine M. McNicholas, Paul D. |
| contents | Zhu and Melnykov (2018) develop a model to fit mixture models when the components are derived from the Manly transformation. Their EM algorithm utilizes Nelder-Mead optimization in the M-step to update the skew parameter, $\boldsymbolλ_g$. An alternative EM gradient algorithm is proposed, using one step of Newton's method, when initial estimates for the model parameters are good. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2410_00848 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | An EM Gradient Algorithm for Mixture Models with Components Derived from the Manly Transformation Clark, Katharine M. McNicholas, Paul D. Machine Learning Methodology Zhu and Melnykov (2018) develop a model to fit mixture models when the components are derived from the Manly transformation. Their EM algorithm utilizes Nelder-Mead optimization in the M-step to update the skew parameter, $\boldsymbolλ_g$. An alternative EM gradient algorithm is proposed, using one step of Newton's method, when initial estimates for the model parameters are good. |
| title | An EM Gradient Algorithm for Mixture Models with Components Derived from the Manly Transformation |
| topic | Machine Learning Methodology |
| url | https://arxiv.org/abs/2410.00848 |