标题： 指数化弗雷歇情形下的IAPO估计量 显示英文标题

标题： IAPO estimators in Exponentiated Frechet case

摘要： 2017年，Jordanova及其合作者研究了p-外部值的概率，并随后利用这些概率构建了与分布敏感相关的IPO估计量。 这些研究没有考虑分布的不对称性。 这一不足之处最近已被克服，并定义了相应的偏态p-外部值概率以及所谓的IAPO估计量。 在这里，我们将这些结果应用于Nadarajah和Kotz于2003年引入的指数化Frechet分布。 “IAPO”缩写来源于“偏态p-外部值的逆概率”。 这些估计量使用经验上的偏态$p$-围栏作为辅助特征。 通过这种方式，联系估计参数和偏态$p$-外部值概率的系统有了更简单的解法。 与我们之前关于对应IPO和IPO-NM估计量的研究相比，IAPO估计量在累积分布函数右尾的正则变化指数方面给出了更好的结果。 模拟研究展示了它们的收敛速度，并完成了这项工作。 显示英文摘要

摘要： In 2017 Jordanova and co-authors consider probabilities for p-outside values, and later on, they use them in order to construct distribution sensitive IPO estimators. These works do not take into account the asymmetry of the distribution. This shortcoming was recently overcome and the corresponding probabilities for asymmetric p-outside values, together with the so-called IAPO estimators, were defined. Here we apply these results to Exponentiated-Frechet distribution, introduced in 2003 by Nadarajah and Kotz. The abbreviation "IAPO" comes from "Inverse Probabilities for Asymmetric P-Outside Values". These estimators use as an auxiliary characteristic the empirical asymmetric $p$-fences. In this way, the system relating the estimated parameters and the asymmetric probabilities for $p$-outside values has an easier solution. The comparison with our previous study about the corresponding IPO and IPO-NM estimators shows that IAPO estimators give better results for the index of regular variation of the right tail of the cumulative distribution function. A simulation study depicts their rates of convergence, and finishes this work.