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Maxima of moving maxima of continuous functions

机译:连续函数的移动最大值

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Maxima of moving maxima of continuous functions (CM3) are max-stable processes aimed at modelling extremes of continuous phenomena over time. They are defined as Smith and Weissman's M4 processes with continuous functions rather than vectors. After standardization of the margins of the observed process into unit-Frechet, CM3 processes can model the remaining spatio-temporal dependence structure. CM3 processes have the property of joint regular variation. The spectral processes from this class admit particularly simple expressions given here. Furthermore, depending on the speed with which the parameter functions tend toward zero, CM3 processes fulfill the finite-cluster condition and the strong mixing condition. Processes enjoying these three properties also enjoy a simple expression for their extremal index. Next a method to fit CM3 processes to data is investigated. The first step is to estimate the length of the temporal dependence. Then, by selecting a suitable number of blocks of extremes of this length, clustering algorithms are used to estimate the total number of different profiles. The parameter functions themselves are estimated thanks to the output of the partitioning algorithms. The full procedure only requires one parameter which is the range of variation allowed among the different profiles. The dissimilarity between the original CM3 and the estimated version is evaluated by means of the Hausdorff distance between the graphs of the parameter functions.
机译:连续函数(CM3)的移动最大值的最大值是最大稳定的过程,旨在模拟随时间变化的连续现象的极端情况。它们被定义为具有连续函数而非向量的Smith和Weissman的M4过程。在将观察到的过程的余量标准化为单位弗雷谢后,CM3过程可以对剩余的时空依赖结构建模。 CM3过程具有联合规则变化的属性。此类的光谱过程允许此处给出特别简单的表达式。此外,根据参数函数趋向于零的速度,CM3过程满足有限簇条件和强混合条件。具有这三个特性的过程的极值指标也很简单。接下来,研究一种使CM3流程适合数据的方法。第一步是估计时间依赖性的长度。然后,通过选择适当数量的该长度的极值块,使用聚类算法来估计不同轮廓的总数。借助分区算法的输出,可以估计参数函数本身。整个过程仅需要一个参数,该参数是不同配置文件之间允许的变化范围。原始CM3和估计版本之间的差异通过参数函数图形之间的Hausdorff距离进行评估。

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