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Modeling of the ARMA random effects covariance matrix in logistic random effects models

Author

Listed:
  • Keunbaik Lee

    (Sungkyunkwan University)

  • Hoimin Jung

    (Korea Land & Housing Institute)

  • Jae Keun Yoo

    (Ewha Womans University)

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.

Suggested Citation

  • Keunbaik Lee & Hoimin Jung & Jae Keun Yoo, 2019. "Modeling of the ARMA random effects covariance matrix in logistic random effects models," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 28(2), pages 281-299, June.
    • Handle: RePEc:spr:stmapp:v:28:y:2019:i:2:d:10.1007_s10260-018-00440-y
    DOI: 10.1007/s10260-018-00440-y
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    References listed on IDEAS

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    Cited by:

    1. Lee, Keunbaik & Lee, Chang-Hoon & Kwak, Min-Sun & Jang, Eun Jin, 2021. "Analysis of multivariate longitudinal data using ARMA Cholesky and hypersphere decompositions," Computational Statistics & Data Analysis, Elsevier, vol. 156(C).

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