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High-dimensional Regression Models with Structured Coefficients
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| 자료유형 | 학위논문 |
|---|---|
| 서명/저자사항 | High-dimensional Regression Models with Structured Coefficients. |
| 개인저자 | Li, Yuan. |
| 단체저자명 | The University of Wisconsin - Madison. Statistics. |
| 발행사항 | [S.l.]: The University of Wisconsin - Madison., 2018. |
| 발행사항 | Ann Arbor: ProQuest Dissertations & Theses, 2018. |
| 형태사항 | 124 p. |
| 기본자료 저록 | Dissertation Abstracts International 79-12B(E). Dissertation Abstract International |
| ISBN | 9780438298781 |
| 학위논문주기 | Thesis (Ph.D.)--The University of Wisconsin - Madison, 2018. |
| 일반주기 |
Source: Dissertation Abstracts International, Volume: 79-12(E), Section: B.
Adviser: Garvesh Raskutti. |
| 요약 | Regression models are very common for statistical inference, especially linear regression models with Gaussian noise. But in many modern scientific applications with large-scale datasets, the number of samples is small relative to the number of |
| 요약 | Firstly, most literature provides statistical analysis for high-dimensional linear models with Gaussian noise, it is unclear whether similar results still hold if we are no longer in the Gaussian setting. To answer this question under Poisson se |
| 요약 | Secondly, much of the theory and methodology for high-dimensional linear regression models are based on the assumption that independent variables are independent of each other or have weak correlations. But it is possible that this assumption is |
| 일반주제명 | Statistics. Mathematics. Computer science. |
| 언어 | 영어 |
| 바로가기 | 
: 이 자료의 원문은 한국교육학술정보원에서 제공합니다. |
