| LDR | | 01672nmm uu200385 4500 |
| 001 | | 000000333829 |
| 005 | | 20240805174621 |
| 008 | | 181129s2018 |||||||||||||||||c||eng d |
| 020 | |
▼a 9780438328297 |
| 035 | |
▼a (MiAaPQ)AAI10825583 |
| 035 | |
▼a (MiAaPQ)purdue:22867 |
| 040 | |
▼a MiAaPQ
▼c MiAaPQ
▼d 248032 |
| 082 | 0 |
▼a 310 |
| 100 | 1 |
▼a Liu, Meimei. |
| 245 | 10 |
▼a Computationally Efficient Nonparametric Testing. |
| 260 | |
▼a [S.l.] :
▼b Purdue University.,
▼c 2018 |
| 260 | 1 |
▼a Ann Arbor :
▼b ProQuest Dissertations & Theses,
▼c 2018 |
| 300 | |
▼a 107 p. |
| 500 | |
▼a Source: Dissertation Abstracts International, Volume: 80-01(E), Section: B. |
| 500 | |
▼a Advisers: Guang Cheng |
| 502 | 1 |
▼a Thesis (Ph.D.)--Purdue University, 2018. |
| 520 | |
▼a 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 |
| 520 | |
▼a 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 |
| 590 | |
▼a School code: 0183. |
| 650 | 4 |
▼a Statistics. |
| 690 | |
▼a 0463 |
| 710 | 20 |
▼a Purdue University.
▼b Statistics. |
| 773 | 0 |
▼t Dissertation Abstracts International
▼g 80-01B(E). |
| 773 | |
▼t Dissertation Abstract International |
| 790 | |
▼a 0183 |
| 791 | |
▼a Ph.D. |
| 792 | |
▼a 2018 |
| 793 | |
▼a English |
| 856 | 40 |
▼u http://www.riss.kr/pdu/ddodLink.do?id=T14998781
▼n KERIS |
| 980 | |
▼a 201812
▼f 2019 |
| 990 | |
▼a 관리자 |