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Local robust and asymptotically unbiased estimation of conditional Pareto-type tails

机译:条件帕累托型尾部的局部鲁棒和渐近无偏估计

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摘要

We introduce a non-parametric robust and asymptotically unbiased estimator for the tail index of a conditional Pareto-type response distribution in presence of random covariates. The estimator is obtained from local fits of the extended Pareto distribution to the relative excesses over a high threshold using an adjusted minimum density power divergence estimation technique. We derive the asymptotic properties of the proposed estimator under some mild regularity conditions, and also investigate its finite sample performance with a small simulation experiment. The practical applicability of the methodology is illustrated on a dataset of calcium content measurements of soil samples.
机译:我们为存在随机协变量的条件帕累托型响应分布的尾部索引引入了非参数鲁棒且渐近无偏估计量。使用调整后的最小密度功率发散估计技术,可从扩展的帕累托分布对较高阈值上的相对过剩的局部拟合获得估计量。我们推导了该估计量在某些适度规律性条件下的渐近性质,并通过一个小型模拟实验研究了其有限样本性能。该方法的实际适用性在土壤样品钙含量测量数据集上进行了说明。

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