자료유형 | E-Book |
---|---|
개인저자 | Liu, Meimei. |
단체저자명 | Purdue University. Statistics. |
서명/저자사항 | Computationally Efficient Nonparametric Testing. |
발행사항 | [S.l.] : Purdue University., 2018 |
발행사항 | Ann Arbor : ProQuest Dissertations & Theses, 2018 |
형태사항 | 107 p. |
소장본 주기 | School code: 0183. |
ISBN | 9780438328297 |
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 |
기본자료 저록 | Dissertation Abstracts International80-01B(E). Dissertation Abstract International |
대출바로가기 | http://www.riss.kr/pdu/ddodLink.do?id=T14998781 |