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Variable selection and estimation in generalized linear models with the seamless L(0) penalty
In this paper, we propose variable selection and estimation in generalized linear models using the seamless L(0) (SELO) penalized likelihood approach. The SELO penalty is a smooth function that very closely resembles the discontinuous L(0) penalty. We develop an e cient algorithm to fit the model, a...
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| Main Authors: | , , |
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| Format: | Artigo |
| Sprog: | Inglês |
| Udgivet: |
2012
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| Fag: | |
| Online adgang: | https://ncbi.nlm.nih.gov/pmc/articles/PMC3600656/ https://ncbi.nlm.nih.gov/pubmed/23519603 https://ncbi.nlm.nih.govhttp://dx.doi.org/10.1002/cjs.11165 |
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