Two-step variable selection in quantile regression models
We propose a two-step variable selection procedure for high dimensional quantile regressions, in which the dimension of the covariates,<i> p<sub>n</sub></i> is much larger than the sample size <i>n</i>. In the first step, we perform <i>ℓ</i><sub>1</sub> penalty, and we demonstrate that the first ste...
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| Hovedforfatter: | |
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| Format: | Artigo |
| Sprog: | Inglês |
| Udgivet: |
Academic Journals Center of Shanghai Normal University
2015-06-01
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| Serier: | 上海师范大学学报. 自然科学版 |
| Fag: | |
| Online adgang: | http://qktg.shnu.edu.cn/zrb/shsfqkszrb/ch/reader/create_pdf.aspx?file_no=201503005&flag=1&year_id=2015&quarter_id=3 |
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