MARC보기
LDR02115nmm uu200445 4500
001000000331594
00520240805164711
008181129s2018 |||||||||||||||||c||eng d
020 ▼a 9780438298781
035 ▼a (MiAaPQ)AAI10931241
035 ▼a (MiAaPQ)wisc:15642
040 ▼a MiAaPQ ▼c MiAaPQ ▼d 248032
0820 ▼a 310
1001 ▼a Li, Yuan.
24510 ▼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.
5021 ▼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
71020 ▼a The University of Wisconsin - Madison. ▼b Statistics.
7730 ▼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
85640 ▼u http://www.riss.kr/pdu/ddodLink.do?id=T15001016 ▼n KERIS
980 ▼a 201812 ▼f 2019
990 ▼a 관리자