Autori
Lee, Keunbaik
Jung, Hoimin
Keun-Chang, Yoon

Titolo
Modeling of the ARMA random effects covariance matrix in logistic random effects models
Periodico
Statistical methods & applications : Journal of the Italian Statistical Society
Anno: 2019 - Volume: 28 - Fascicolo: 2 - Pagina iniziale: 281 - Pagina finale: 299

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.



SICI: 1618-2510(2019)28:2<281:MOTARE>2.0.ZU;2-8

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