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Data-adaptive Shrinkage via the Hyperpenalized EM Algorithm
We propose an extension of the expectation-maximization (EM) algorithm, called the hyperpenalized EM (HEM) algorithm, that maximizes a penalized log-likelihood, for which some data are missing or unavailable, using a data-adaptive estimate of the penalty parameter. This is potentially useful in appl...
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| Publicado no: | Stat Biosci |
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| Main Authors: | , , |
| Formato: | Artigo |
| Idioma: | Inglês |
| Publicado em: |
2015
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| Assuntos: | |
| Acesso em linha: | https://ncbi.nlm.nih.gov/pmc/articles/PMC4728141/ https://ncbi.nlm.nih.gov/pubmed/26834856 https://ncbi.nlm.nih.govhttp://dx.doi.org/10.1007/s12561-015-9132-x |
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