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Computing Leapfrog Regularization Paths with Applications to Large-Scale K-mer Logistic Regression
High-dimensional statistics deals with statistical inference when the number of parameters or features p exceeds the number of observations n (i.e., [Formula: see text]). In this case, the parameter space must be constrained either by regularization or by selecting a small subset of [Formula: see te...
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| 出版年: | J Comput Biol |
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| 第一著者: | |
| フォーマット: | Artigo |
| 言語: | Inglês |
| 出版事項: |
Mary Ann Liebert, Inc., publishers
2021
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| 主題: | |
| オンライン･アクセス: | https://ncbi.nlm.nih.gov/pmc/articles/PMC8219187/ https://ncbi.nlm.nih.gov/pubmed/33739865 https://ncbi.nlm.nih.govhttp://dx.doi.org/10.1089/cmb.2020.0284 |
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