| LDR | | 01805nam u200385 4500 |
| 001 | | 000000422608 |
| 005 | | 20190215170249 |
| 008 | | 181129s2017 |||||||||||||||||c||eng d |
| 020 | |
▼a 9780438309739 |
| 035 | |
▼a (MiAaPQ)AAI10970732 |
| 035 | |
▼a (MiAaPQ)OhioLINK:osu1497966698387606 |
| 040 | |
▼a MiAaPQ
▼c MiAaPQ
▼d 247004 |
| 082 | 0 |
▼a 310 |
| 100 | 1 |
▼a Gory, Jeffrey J. |
| 245 | 10 |
▼a Marginally Interpretable Generalized Linear Mixed Models. |
| 260 | |
▼a [S.l.]:
▼b The Ohio State University.,
▼c 2017. |
| 260 | 1 |
▼a Ann Arbor:
▼b ProQuest Dissertations & Theses,
▼c 2017. |
| 300 | |
▼a 178 p. |
| 500 | |
▼a Source: Dissertation Abstracts International, Volume: 80-01(E), Section: B. |
| 500 | |
▼a Advisers: Peter Craigmile |
| 502 | 1 |
▼a Thesis (Ph.D.)--The Ohio State University, 2017. |
| 520 | |
▼a A popular approach for relating correlated measurements of a non-Gaussian response variable to a set of predictors is to introduce latent random variables and fit a generalized linear mixed model. The conventional strategy for specifying such a |
| 520 | |
▼a We define a class of marginally interpretable generalized linear mixed models that lead to parameter estimates with a marginal interpretation while maintaining the desirable statistical properties of a conditionally specified model. The distingu |
| 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 80-01B(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=T15001339
▼n KERIS
▼z 이 자료의 원문은 한국교육학술정보원에서 제공합니다. |
| 980 | |
▼a 201812
▼f 2019 |
| 990 | |
▼a ***1012033 |