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A variant of sparse partial least squares for variable selection and data exploration
When data are sparse and/or predictors multicollinear, current implementation of sparse partial least squares (SPLS) does not give estimates for non-selected predictors nor provide a measure of inference. In response, an approach termed “all-possible” SPLS is proposed, which fits a SPLS model for al...
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| Главные авторы: | , , , , , , , , , |
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| Формат: | Artigo |
| Язык: | Inglês |
| Опубликовано: |
Frontiers Media S.A.
2014
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| Предметы: | |
| Online-ссылка: | https://ncbi.nlm.nih.gov/pmc/articles/PMC3939647/ https://ncbi.nlm.nih.gov/pubmed/24624079 https://ncbi.nlm.nih.govhttp://dx.doi.org/10.3389/fninf.2014.00018 |
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