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Variable Screening and Inference Problems for High Dimensional Data
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| 자료유형 | 학위논문 |
|---|---|
| 서명/저자사항 | Variable Screening and Inference Problems for High Dimensional Data. |
| 개인저자 | Zhang, Jingsi Joyce. |
| 단체저자명 | Northwestern University. Statistics. |
| 발행사항 | [S.l.]: Northwestern University., 2018. |
| 발행사항 | Ann Arbor: ProQuest Dissertations & Theses, 2018. |
| 형태사항 | 138 p. |
| 기본자료 저록 | Dissertation Abstracts International 79-12B(E). Dissertation Abstract International |
| ISBN | 9780438332102 |
| 학위논문주기 | Thesis (Ph.D.)--Northwestern University, 2018. |
| 일반주기 |
Source: Dissertation Abstracts International, Volume: 79-12(E), Section: B.
Adviser: Joel Horowitz. |
| 요약 | This dissertation focuses on variable screening for ultra-high dimensional data and inference for comparatively-high dimensional data. I explore two specific problems in this area, which are motivated by real data examples, and discuss the motiv |
| 요약 | Chapter 1 introduces a new metric, the so-called martingale difference correlation, to measure the departure of conditional mean independence between a scalar response variable Y and a vector predictor variable X. Our metric is a natural extens |
| 요약 | In Chapter 2, we propose a general method for constructing confidence intervals and statistical tests for single or low-dimensional components of a large parameter vector in a high-dimensional linear model, where the dimension of the regression |
| 일반주제명 | Statistics. |
| 언어 | 영어 |
| 바로가기 | 
: 이 자료의 원문은 한국교육학술정보원에서 제공합니다. |
