자료유형 | 학위논문 |
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서명/저자사항 | Robust Dependence-adjusted Methods for High Dimensional Data. |
개인저자 | Bose, Koushiki. |
단체저자명 | Princeton University. Operations Research and Financial Engineering. |
발행사항 | [S.l.]: Princeton University., 2018. |
발행사항 | Ann Arbor: ProQuest Dissertations & Theses, 2018. |
형태사항 | 216 p. |
기본자료 저록 | Dissertation Abstracts International 79-10B(E). Dissertation Abstract International |
ISBN | 9780438047945 |
학위논문주기 | Thesis (Ph.D.)--Princeton University, 2018. |
일반주기 |
Source: Dissertation Abstracts International, Volume: 79-10(E), Section: B.
Adviser: Jianqing Fan. |
요약 | 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. |
요약 | 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 |
일반주제명 | Statistics. |
언어 | 영어 |
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: 이 자료의 원문은 한국교육학술정보원에서 제공합니다. |