Bayesian baseline-category logit random effects models for longitudinal nominal data
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Kim, Jiyeong | - |
dc.contributor.author | Lee, Keunbaik | - |
dc.date.accessioned | 2023-11-14T08:27:48Z | - |
dc.date.available | 2023-11-14T08:27:48Z | - |
dc.date.created | 2023-10-05 | - |
dc.date.issued | 2020-03 | - |
dc.identifier.issn | 2287-7843 | - |
dc.identifier.uri | https://scholarworks.bwise.kr/hanyang/handle/2021.sw.hanyang/192240 | - |
dc.description.abstract | Baseline-category logit random effects models have been used to analyze longitudinal nominal data. The models account for subject-specific variations using random effects. However, the random effects covariance matrix in the models needs to explain subject-specific variations as well as serial correlations for nominal outcomes. In order to satisfy them, the covariance matrix must be heterogeneous and high-dimensional. However, it is difficult to estimate the random effects covariance matrix due to its high dimensionality and positive-definiteness. In this paper, we exploit the modified Cholesky decomposition to estimate the high-dimensional heterogeneous random effects covariance matrix. Bayesian methodology is proposed to estimate parameters of interest. The proposed methods are illustrated with real data from the McKinney Homeless Research Project. | - |
dc.language | 영어 | - |
dc.language.iso | en | - |
dc.publisher | Korean Statistical SocietyRoom 803, 88, Gasan digital 1-ro, Geumcheon-guSeoul08590office@kss.or.kr | - |
dc.title | Bayesian baseline-category logit random effects models for longitudinal nominal data | - |
dc.type | Article | - |
dc.contributor.affiliatedAuthor | Kim, Jiyeong | - |
dc.identifier.doi | 10.29220/CSAM.2020.27.2.201 | - |
dc.identifier.scopusid | 2-s2.0-85086834372 | - |
dc.identifier.wosid | 000526960000004 | - |
dc.identifier.bibliographicCitation | Communications for Statistical Applications and Methods, v.27, no.2, pp.201 - 210 | - |
dc.relation.isPartOf | Communications for Statistical Applications and Methods | - |
dc.citation.title | Communications for Statistical Applications and Methods | - |
dc.citation.volume | 27 | - |
dc.citation.number | 2 | - |
dc.citation.startPage | 201 | - |
dc.citation.endPage | 210 | - |
dc.type.rims | ART | - |
dc.identifier.kciid | ART002575986 | - |
dc.description.journalClass | 1 | - |
dc.description.isOpenAccess | Y | - |
dc.description.journalRegisteredClass | scopus | - |
dc.description.journalRegisteredClass | kci | - |
dc.relation.journalResearchArea | Mathematics | - |
dc.relation.journalWebOfScienceCategory | Statistics & Probability | - |
dc.subject.keywordPlus | REGRESSION-MODELS | - |
dc.subject.keywordAuthor | covariance matrix | - |
dc.subject.keywordAuthor | heterogeneous | - |
dc.subject.keywordAuthor | high-dimensional | - |
dc.subject.keywordAuthor | modified Cholesky decomposition | - |
dc.subject.keywordAuthor | positive-definiteness | - |
dc.identifier.url | http://www.csam.or.kr/journal/view.html?doi=10.29220/CSAM.2020.27.2.201 | - |
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