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Computationally Efficient Nonparametric Testing
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| 자료유형 | 학위논문 |
|---|---|
| 서명/저자사항 | Computationally Efficient Nonparametric Testing. |
| 개인저자 | Liu, Meimei. |
| 단체저자명 | Purdue University. Statistics. |
| 발행사항 | [S.l.]: Purdue University., 2018. |
| 발행사항 | Ann Arbor: ProQuest Dissertations & Theses, 2018. |
| 형태사항 | 107 p. |
| 기본자료 저록 | Dissertation Abstracts International 80-01B(E). Dissertation Abstract International |
| ISBN | 9780438328297 |
| 학위논문주기 | Thesis (Ph.D.)--Purdue University, 2018. |
| 일반주기 |
Source: Dissertation Abstracts International, Volume: 80-01(E), Section: B.
Advisers: Guang Cheng |
| 요약 | A common challenge in nonparametric inference is its high computational complexity when data volume is large. In this thesis, I will introduce novel computationally efficient nonparametric testing methods. Firstly, we develop a computationally e |
| 요약 | Secondly, we study nonparametric testing under algorithmic regularization. Early stopping of iterative algorithms is an algorithmic regularization method to avoid over-fitting in estimation and classification. In this paper, we show that early s |
| 일반주제명 | Statistics. |
| 언어 | 영어 |
| 바로가기 | 
: 이 자료의 원문은 한국교육학술정보원에서 제공합니다. |
