有序聚类数据的边际化条件模型
Marginalizable conditional model for clustered ordinal data
摘要：
我们引入了一个灵活的参数相关的二进制数据的混合效应模型，参数可以直接解释为边际优势比。这导致一个稳健估计方程，其最佳加权矩阵是真正的基于模型的协方差阵的逆。相关随机效应可以施加灵活的相关结构，相关参数可以通过求解复合似然得分函数来估计。即使在条件参数模型被错误指定时，边缘参数也被一致地估计，并且在正确的模型指定下，与极大似然估计相比，稳健估计过程具有低的估计效率损失。模拟，分析马德拉斯纵向精神分裂症研究和英国社会属性面板调查进行了证明我们的方法。
英文摘要：
We introduce a flexible parametric mixed effects model for correlated binary data, with parameters that can be directly interpreted as marginal odds ratios. This leads to a robust estimation equation with an optimal weighting matrix being the inverse of a genuine model-based covariance matrix. Flexible correlation structures can be imposed by correlated random effects, and correlation parameters can be estimated by solving a composite likelihood score function. Marginal parameters are consistently estimated even when the conditional parametric model is misspecified, and the robust estimation procedure has low estimation efficiency loss compared to the maximum likelihood estimation under a correct model specification. Simulations, analyses of the Madras longitudinal schizophrenia study and British social attributes panel survey were carried out to demonstrate our method.
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