| LDR | | 00000nmm u2200205 4500 |
| 001 | | 000000330679 |
| 005 | | 20241104142839 |
| 008 | | 181129s2017 ||| | | | eng d |
| 020 | |
▼a 9780438098428 |
| 035 | |
▼a (MiAaPQ)AAI10901903 |
| 035 | |
▼a (MiAaPQ)OhioLINK:osu1503072235693181 |
| 040 | |
▼a MiAaPQ
▼c MiAaPQ
▼d 248032 |
| 049 | 1 |
▼f DP |
| 082 | 0 |
▼a 310 |
| 100 | 1 |
▼a Jiang, Jieyi. |
| 245 | 10 |
▼a Realistic Predictive Risk: The Role of Penalty and Covariate Diffusion in Model Selection. |
| 260 | |
▼a [S.l.] :
▼b The Ohio State University.,
▼c 2017 |
| 260 | 1 |
▼a Ann Arbor :
▼b ProQuest Dissertations & Theses,
▼c 2017 |
| 300 | |
▼a 176 p. |
| 500 | |
▼a Source: Dissertation Abstracts International, Volume: 79-12(E), Section: B. |
| 500 | |
▼a Advisers: Steven MacEachern |
| 502 | 1 |
▼a Thesis (Ph.D.)--The Ohio State University, 2017. |
| 520 | |
▼a One important goal of model selection is the minimization of predictive risk. First, fold-wise cross-validation aims at estimating the predictive risk and identifying the structure of the linear model. However, it leads to inconsistent model sel |
| 520 | |
▼a The second part of this dissertation discusses the change in predictive risk due to diffused covariates. Typical derivations for model selection criteria assume that the distributions of covariates in the training set and future-prediction set a |
| 590 | |
▼a School code: 0168. |
| 650 | 4 |
▼a Statistics. |
| 690 | |
▼a 0463 |
| 710 | 20 |
▼a The Ohio State University.
▼b Statistics. |
| 773 | 0 |
▼t Dissertation Abstracts International
▼g 79-12B(E). |
| 773 | |
▼t Dissertation Abstract International |
| 790 | |
▼a 0168 |
| 791 | |
▼a Ph.D. |
| 792 | |
▼a 2017 |
| 793 | |
▼a English |
| 856 | 40 |
▼u http://www.riss.kr/pdu/ddodLink.do?id=T15000314
▼n KERIS |
| 980 | |
▼a 201812
▼f 2019 |
| 990 | |
▼a 관리자
▼b 관리자 |