Full metadata record
DC Field | Value | Language |
---|---|---|
dc.contributor.author | 유재근 | * |
dc.date.accessioned | 2019-07-01T16:30:04Z | - |
dc.date.available | 2019-07-01T16:30:04Z | - |
dc.date.issued | 2019 | * |
dc.identifier.issn | 1618-2510 | * |
dc.identifier.issn | 1613-981X | * |
dc.identifier.other | OAK-24925 | * |
dc.identifier.uri | https://dspace.ewha.ac.kr/handle/2015.oak/250025 | - |
dc.description.abstract | Logistic random effects models (LREMs) have been frequently used to analyze longitudinal binary data. When a random effects covariance matrix is used to make proper inferences on covariate effects, the random effects in the models account for both within-subject association and between-subject variation, but the covariance matix is difficult to estimate because it is high-dimensional and should be positive definite. To overcome these limitations, two Cholesky decomposition approaches were proposed for precision matrix and covariance matrix: modified Cholesky decomposition and moving average Cholesky decomposition, respectively. However, the two approaches may not work when there are non-trivial and complicated correlations of repeated outcomes. In this paper, we combined the two decomposition approaches to model the random effects covariance matrix in the LREMs, thereby capturing a wider class of sophisticated dependence structures while achieving parsimony in parametrization. We then used our proposed model to analyze lung cancer data. | * |
dc.language | English | * |
dc.publisher | SPRINGER HEIDELBERG | * |
dc.subject | Cholesky decomposition | * |
dc.subject | Longitudinal data | * |
dc.subject | Heteroscedastic | * |
dc.subject | Repeated outcomes | * |
dc.title | Modeling of the ARMA random effects covariance matrix in logistic random effects models | * |
dc.type | Article | * |
dc.relation.issue | 2 | * |
dc.relation.volume | 28 | * |
dc.relation.index | SCIE | * |
dc.relation.index | SCOPUS | * |
dc.relation.startpage | 281 | * |
dc.relation.lastpage | 299 | * |
dc.relation.journaltitle | STATISTICAL METHODS AND APPLICATIONS | * |
dc.identifier.doi | 10.1007/s10260-018-00440-y | * |
dc.identifier.wosid | WOS:000468997800005 | * |
dc.author.google | Lee, Keunbaik | * |
dc.author.google | Jung, Hoimin | * |
dc.author.google | Yoo, Jae Keun | * |
dc.contributor.scopusid | 유재근(23032759600) | * |
dc.date.modifydate | 20240130113500 | * |