| LDR | | 02191nam u200445 4500 |
| 001 | | 000000419815 |
| 005 | | 20190215163958 |
| 008 | | 181129s2018 |||||||||||||||||c||eng d |
| 020 | |
▼a 9780438298781 |
| 035 | |
▼a (MiAaPQ)AAI10931241 |
| 035 | |
▼a (MiAaPQ)wisc:15642 |
| 040 | |
▼a MiAaPQ
▼c MiAaPQ
▼d 247004 |
| 082 | 0 |
▼a 310 |
| 100 | 1 |
▼a Li, Yuan. |
| 245 | 10 |
▼a High-dimensional Regression Models with Structured Coefficients. |
| 260 | |
▼a [S.l.]:
▼b The University of Wisconsin - Madison.,
▼c 2018. |
| 260 | 1 |
▼a Ann Arbor:
▼b ProQuest Dissertations & Theses,
▼c 2018. |
| 300 | |
▼a 124 p. |
| 500 | |
▼a Source: Dissertation Abstracts International, Volume: 79-12(E), Section: B. |
| 500 | |
▼a Adviser: Garvesh Raskutti. |
| 502 | 1 |
▼a Thesis (Ph.D.)--The University of Wisconsin - Madison, 2018. |
| 520 | |
▼a Regression models are very common for statistical inference, especially linear regression models with Gaussian noise. But in many modern scientific applications with large-scale datasets, the number of samples is small relative to the number of |
| 520 | |
▼a Firstly, most literature provides statistical analysis for high-dimensional linear models with Gaussian noise, it is unclear whether similar results still hold if we are no longer in the Gaussian setting. To answer this question under Poisson se |
| 520 | |
▼a Secondly, much of the theory and methodology for high-dimensional linear regression models are based on the assumption that independent variables are independent of each other or have weak correlations. But it is possible that this assumption is |
| 590 | |
▼a School code: 0262. |
| 650 | 4 |
▼a Statistics. |
| 650 | 4 |
▼a Mathematics. |
| 650 | 4 |
▼a Computer science. |
| 690 | |
▼a 0463 |
| 690 | |
▼a 0405 |
| 690 | |
▼a 0984 |
| 710 | 20 |
▼a The University of Wisconsin - Madison.
▼b Statistics. |
| 773 | 0 |
▼t Dissertation Abstracts International
▼g 79-12B(E). |
| 773 | |
▼t Dissertation Abstract International |
| 790 | |
▼a 0262 |
| 791 | |
▼a Ph.D. |
| 792 | |
▼a 2018 |
| 793 | |
▼a English |
| 856 | 40 |
▼u http://www.riss.kr/pdu/ddodLink.do?id=T15001016
▼n KERIS
▼z 이 자료의 원문은 한국교육학술정보원에서 제공합니다. |
| 980 | |
▼a 201812
▼f 2019 |
| 990 | |
▼a ***1012033 |