| LDR | | 01707nmm uu200385 4500 |
| 001 | | 000000333279 |
| 005 | | 20240805172730 |
| 008 | | 181129s2018 |||||||||||||||||c||eng d |
| 020 | |
▼a 9780438047945 |
| 035 | |
▼a (MiAaPQ)AAI10815651 |
| 035 | |
▼a (MiAaPQ)princeton:12527 |
| 040 | |
▼a MiAaPQ
▼c MiAaPQ
▼d 248032 |
| 082 | 0 |
▼a 310 |
| 100 | 1 |
▼a Bose, Koushiki. |
| 245 | 10 |
▼a Robust Dependence-adjusted Methods for High Dimensional Data. |
| 260 | |
▼a [S.l.] :
▼b Princeton University.,
▼c 2018 |
| 260 | 1 |
▼a Ann Arbor :
▼b ProQuest Dissertations & Theses,
▼c 2018 |
| 300 | |
▼a 216 p. |
| 500 | |
▼a Source: Dissertation Abstracts International, Volume: 79-10(E), Section: B. |
| 500 | |
▼a Adviser: Jianqing Fan. |
| 502 | 1 |
▼a Thesis (Ph.D.)--Princeton University, 2018. |
| 520 | |
▼a The focus of this dissertation is the development, implementation and verification of robust methods for high dimensional heavy-tailed data, with an emphasis on underlying dependence-adjustment through factor models. |
| 520 | |
▼a First, we prove a nonasymptotic version of the Bahadur representation for a Huber loss M-estimator in the presence of heavy-tailed errors. Consequently, we prove a number of important normal approximation results, including the Berry-Esseen boun |
| 590 | |
▼a School code: 0181. |
| 650 | 4 |
▼a Statistics. |
| 690 | |
▼a 0463 |
| 710 | 20 |
▼a Princeton University.
▼b Operations Research and Financial Engineering. |
| 773 | 0 |
▼t Dissertation Abstracts International
▼g 79-10B(E). |
| 773 | |
▼t Dissertation Abstract International |
| 790 | |
▼a 0181 |
| 791 | |
▼a Ph.D. |
| 792 | |
▼a 2018 |
| 793 | |
▼a English |
| 856 | 40 |
▼u http://www.riss.kr/pdu/ddodLink.do?id=T14998189
▼n KERIS |
| 980 | |
▼a 201812
▼f 2019 |
| 990 | |
▼a 관리자 |