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

机译：arma随机效应协方差矩阵在逻辑随机效应模型中的建模

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摘要

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.
机译：Logistic随机效果模型（LREM）经常用于分析纵向二进制数据。当随机效应协方差矩阵用于对协变量的效果进行适当的推论时，模型中的随机效果占对象内关联内部和对象变异之间的，但是协方差难以估计，因为它是高维的应该是积极的。为了克服这些局限，提出了两种Cholesky分解方法，用于精密矩阵和协方差矩阵：改性孔基分解和移动平均弦孔分解。然而，当反复结果的非琐碎和复杂相关时，这两种方法可能无法工作。在本文中，我们组合了两个分解方法来模拟LREM中的随机效应协方差矩阵，从而捕获更广泛的复杂依赖结构，同时在参数化中实现规定。然后我们使用所提出的模型来分析肺癌数据。

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