CAJ | 학술논문

在本文中，我们比较了广义估计方程中相关阵基于高斯伪似然、修正的高斯伪似然和经验似然的选择方法。通过大量的模拟研究，我们发现修正的高斯伪似然方法优于其他两种方法。对二项离散模型，经验似然方法在选择可交换相关结构时有更好的表现。最后，通过两个实例分析，进一步分析了各个选择方法之间的优劣性。
재본문중，아문비교료엄의고계방정중상관진기우고사위사연、수정적고사위사연화경험사연적선택방법。통과대량적모의연구，아문발현수정적고사위사연방법우우기타량충방법。대이항리산모형，경험사연방법재선택가교환상관결구시유경호적표현。최후，통과량개실례분석，진일보분석료각개선택방법지간적우렬성。
In this paper, we compare two modified Gaussian pseudolikelihood criteria (GPCs) with ex-isting Gaussian pseudolikelihood criterion and empirical likelihood based criteria to choose the working correlation matrix in generalized estimating equations approach. Rich simulation studies are conducted to investigate the performance of these criteria under a range of model settings. The results show that the modified criteria outperform the original GPC and empirical likelihood based criteria in most cases in terms of selection accuracy. Empirical likelihood based criteria perform better to identify exchangeable structure in data with binary response. In the end, these criteria are applied to epilepsy seizure and Madras longitudinal schizophrenia study clinical data sets analysis.