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Weighted composite likelihood-based tests for space-time separability of covariance functions

机译：基于加权复合似然的协方差函数时空可分性检验

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Testing for separability of space-time covariance functions is of great interest in the analysis of space-time data. In this paper we work in a parametric framework and consider the case when the parameter identifying the case of separability of the associated space-time covariance lies on the boundary of the parametric space. This situation is frequently encountered in space-time geostatistics. It is known that classical methods such as likelihood ratio test may fail in this case.rnWe present two tests based on weighted composite likelihood estimates and the bootstrap method, and evaluate their performance through an extensive simulation study as well as an application to Irish wind speeds. The tests are performed with respect to a new class of covariance functions, which presents some desirable mathematical features and has margins of the Generalized Cauchy type. We also apply the test on a element of the Gneiting class, obtaining concordant results.
机译：时空协方差函数的可分性测试在时空数据分析中非常重要。在本文中，我们在参数框架中工作，并考虑当标识关联时空协方差的可分性情况的参数位于参数空间的边界时的情况。这种情况在时空地统计学中经常遇到。众所周知，在这种情况下经典方法（如似然比测试）可能会失败。我们基于加权复合似然估计和自举法提出两种测试，并通过广泛的仿真研究以及其在爱尔兰风速中的应用来评估其性能。 。这些测试是针对一类新的协方差函数执行的，该协方差函数具有一些合乎需要的数学特征，并且具有通用柯西类型的余量。我们还将测试应用于Gneiting类的某个元素上，以获得一致的结果。

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