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Geometric Methods in Statistics and Optimization
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| 자료유형 | 학위논문 |
|---|---|
| 서명/저자사항 | Geometric Methods in Statistics and Optimization. |
| 개인저자 | Wong, Sze Wai. |
| 단체저자명 | The University of Chicago. Statistics. |
| 발행사항 | [S.l.]: The University of Chicago., 2018. |
| 발행사항 | Ann Arbor: ProQuest Dissertations & Theses, 2018. |
| 형태사항 | 114 p. |
| 기본자료 저록 | Dissertation Abstracts International 79-11B(E). Dissertation Abstract International |
| ISBN | 9780438084421 |
| 학위논문주기 | Thesis (Ph.D.)--The University of Chicago, 2018. |
| 일반주기 |
Source: Dissertation Abstracts International, Volume: 79-11(E), Section: B.
Adviser: Lek-Heng Lim. |
| 요약 | Statistical estimation problems in multivariate analysis and machine learning often seek linear relations among variables. This translates to finding an affine subspace from the sample data set that, in an appropriate sense, either best represen |
| 요약 | We then extend the framework to a nest of linear subspaces, that represent the variables in different regimes. Diving into the multi-scale representation of the data revealed by these problems requires a systematic study of nest of linear subspa |
| 요약 | Lastly, we study the Yates's algorithm that was first proposed to exploit the structure of full factorial designed experiment to obtain least squares estimates for factor effects for all factors and their relevant interactions. In short it is an |
| 일반주제명 | Statistics. Applied mathematics. Mathematics. |
| 언어 | 영어 |
| 바로가기 | 
: 이 자료의 원문은 한국교육학술정보원에서 제공합니다. |
