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Robust Adaptive Lasso method for parameter’s estimation and variable selection in high-dimensional sparse models
High dimensional data are commonly encountered in various scientific fields and pose great challenges to modern statistical analysis. To address this issue different penalized regression procedures have been introduced in the litrature, but these methods cannot cope with the problem of outliers and...
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| Yayımlandı: | PLoS One |
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| Asıl Yazarlar: | , , |
| Materyal Türü: | Artigo |
| Dil: | Inglês |
| Baskı/Yayın Bilgisi: |
Public Library of Science
2017
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| Konular: | |
| Online Erişim: | https://ncbi.nlm.nih.gov/pmc/articles/PMC5573134/ https://ncbi.nlm.nih.gov/pubmed/28846717 https://ncbi.nlm.nih.govhttp://dx.doi.org/10.1371/journal.pone.0183518 |
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