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Weighted cumulative sum tests for random effect models with binary responses

机译:随机效应模型与二进制响应的加权累积和测试

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Correlated binary responses are very commonly encountered in many disciplines like, for example, medical studies. The development of goodness-of-fit tests is essential for examining the adequacy of the fitted models. The objective of this article is to provide weighted modifications of cumulative sums or moving cumulative sums of residuals for testing goodness-of-fit of random effects logistic regression models. The proposed weights can be interpreted as the residuals of a weighted linear regression of an omitted covariate on the covariates already included in the fixed part of the model. These processes lead to supremum statistics whose null distribution is derived using simulation. Results from a simulation study suggest better performance of the weighted when compared to the unweighted supremum statistics. The proposed tests are illustrated using a real data example.
机译:在许多学科中非常常见的相关二元响应,例如医学研究。 健美测试的开发对于检查拟合模型的充分性是必不可少的。 本文的目的是提供累积总和的加权修改或移动残留的累积总和,以测试随机效应逻辑回归模型的高度适合。 所提出的重量可以被解释为已经包括在模型的固定部分中的协变量的省略的协变量的加权线性回归的残差。 这些过程导致了使用模拟导出空分布的超级统计数据。 仿真研究结果表明,与未加权的超高统计数据相比,加权的更好性能。 使用真实数据示例说明所提出的测试。

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