| LDR | | 01988nmm uu200397 4500 |
| 001 | | 000000333491 |
| 005 | | 20240805173130 |
| 008 | | 181129s2018 |||||||||||||||||c||eng d |
| 020 | |
▼a 9780438332102 |
| 035 | |
▼a (MiAaPQ)AAI10821605 |
| 035 | |
▼a (MiAaPQ)northwestern:14165 |
| 040 | |
▼a MiAaPQ
▼c MiAaPQ
▼d 248032 |
| 082 | 0 |
▼a 310 |
| 100 | 1 |
▼a Zhang, Jingsi Joyce. |
| 245 | 10 |
▼a Variable Screening and Inference Problems for High Dimensional Data. |
| 260 | |
▼a [S.l.] :
▼b Northwestern University.,
▼c 2018 |
| 260 | 1 |
▼a Ann Arbor :
▼b ProQuest Dissertations & Theses,
▼c 2018 |
| 300 | |
▼a 138 p. |
| 500 | |
▼a Source: Dissertation Abstracts International, Volume: 79-12(E), Section: B. |
| 500 | |
▼a Adviser: Joel Horowitz. |
| 502 | 1 |
▼a Thesis (Ph.D.)--Northwestern University, 2018. |
| 520 | |
▼a 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 |
| 520 | |
▼a 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 |
| 520 | |
▼a 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 |
| 590 | |
▼a School code: 0163. |
| 650 | 4 |
▼a Statistics. |
| 690 | |
▼a 0463 |
| 710 | 20 |
▼a Northwestern University.
▼b Statistics. |
| 773 | 0 |
▼t Dissertation Abstracts International
▼g 79-12B(E). |
| 773 | |
▼t Dissertation Abstract International |
| 790 | |
▼a 0163 |
| 791 | |
▼a Ph.D. |
| 792 | |
▼a 2018 |
| 793 | |
▼a English |
| 856 | 40 |
▼u http://www.riss.kr/pdu/ddodLink.do?id=T14998393
▼n KERIS |
| 980 | |
▼a 201812
▼f 2019 |
| 990 | |
▼a 관리자 |