Modeling of the ARMA random effects covariance matrix in logistic random effects models
- 주제(키워드) Cholesky decomposition , Longitudinal data , Heteroscedastic , Repeated outcomes
- 주제(기타) Statistics & Probability
- 설명문(일반) [Lee, Keunbaik] Sungkyunkwan Univ, Dept Stat, Seoul 03063, South Korea; [Jung, Hoimin] Korea Land & Housing Inst, Terr & Reg Res Dept, Daejeon 34047, South Korea; [Yoo, Jae Keun] Ewha Womans Univ, Dept Stat, Seoul 03760, South Korea
- 등재 SCIE, SCOPUS
- 발행기관 SPRINGER HEIDELBERG
- 발행년도 2019
- URI http://www.dcollection.net/handler/ewha/000000160176
- 본문언어 영어
- Published As http://dx.doi.org/10.1007/s10260-018-00440-y
초록/요약
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.
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