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On a hybrid data cloning method and its application in generalized linear mixed models

机译:混合数据克隆方法及其在广义线性混合模型中的应用

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摘要

The data cloning method is a new computational tool for computing maximum likelihood estimates in complex statistical models such as mixed models. This method is synthesized with integrated nested Laplace approximation to compute maximum likelihood estimates efficiently via a fast implementation in generalized linear mixed models. Asymptotic behavior of the hybrid data cloning method is discussed. The performance of the proposed method is illustrated through a simulation study and real examples. It is shown that the proposed method performs well and rightly justifies the theory. Supplemental materials for this article are available online.
机译:数据克隆方法是一种用于在诸如混合模型之类的复杂统计模型中计算最大似然估计的新计算工具。该方法与集成的嵌套Laplace逼近综合在一起,可以通过在广义线性混合模型中快速实施而有效地计算最大似然估计。讨论了混合数据克隆方法的渐近行为。通过仿真研究和实际算例说明了该方法的性能。结果表明,所提出的方法性能良好,证明了该理论的正确性。可在线获得本文的补充材料。

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