弱相关下Hill估计量的渐近正态性

Y. Berkoun, K. Boualam
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引用次数: 0

摘要

本文讨论了在Doukhan意义下数据弱相关时Hill估计量的渐近正态性。在这种情况下的主要结果依赖于观测结果的强混合。这个假设通常是建立这个估计量的渐近性的关键工具。已经做了许多尝试来放宽平稳和混合的假设。放宽这一条件,假设弱依赖性,推广Rootzen和Starica的结果。这种方法比以前的结果需要更少的限制条件。
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Asymptotic Normality of Hill’s Estimator under Weak Dependence
This note is devoted to the asymptotic normality of Hill's estimator when data are weakly dependent in the sense of Doukhan. The primary results on this setting rely on the observations being strong mixing. This assumption is often the key tool for establishing the asymptotic behavior of this estimator. A number of attempts have been made to relax the assumption of stationarity and mixing. Relaxing this condition, and assuming the weak dependence, we extend the results obtained by Rootzen and Starica. This approach requires less restrictive conditions than the previous results.
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Introductory Chapter: Ramifications of Incomplete Knowledge Asymptotic Normality of Hill’s Estimator under Weak Dependence Methods of Russian Patent Analysis Development of Estimation Procedure of Population Mean in Two-Phase Stratified Sampling A Comparative Study of Maximum Likelihood Estimation and Bayesian Estimation for Erlang Distribution and Its Applications
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