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Maximum composite likelihood estimation for spatial extremes models of Brown-Resnick type with application to precipitation data

机译:Brown-Resnick型空间极端模型的最大复合似然估计及其在降水数据中的应用

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摘要

In this study, we consider the maximum composite likelihood estimator for spatial extremes model class of Brown-Resnick type. The composite likelihood is constructed based on the weighted tail empirical process. It is shown that the proposed estimator is consistent and asymptotically normal under some regularity conditions fulfilled by the model class. We conduct Monte Carlo simulations to evaluate the estimator and apply it to the analysis of a precipitation data set.
机译:在这项研究中,我们考虑了 Brown-Resnick 类型的空间极端模型类的最大复合似然估计器。基于加权尾部经验过程构造复合似然。结果表明,所提出的估计量在模型类满足的某些正则性条件下是一致的,并且是渐近正态的。我们进行蒙特卡罗模拟来评估估计器,并将其应用于降水数据集的分析。

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