关联条件下经验和平滑量子估计器的巴哈杜尔表示法

IF 1 4区 数学 Q3 STATISTICS & PROBABILITY
Nour-Eddine Berrahou, Salim Bouzebda, Lahcen Douge
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引用次数: 0

摘要

本文研究了基于相关序列的量化函数经验估计值和伯恩斯坦多项式估计值的 Bahadur 表示。近似率取决于协方差的衰减率,当协方差迅速趋近于零时,近似率会收敛到独立条件下观察到的最优率。作为应用,我们建立了贝里-埃森约束,假设协方差的多项式衰减为 \(O(n^{-1/3})\)。所有这些结果都是在基础分布的一般条件下建立的。此外,我们还利用相关和非混合模型进行了蒙特卡罗模拟,以评估所建议的估计器的有限样本性能。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

The Bahadur Representation for Empirical and Smooth Quantile Estimators Under Association

The Bahadur Representation for Empirical and Smooth Quantile Estimators Under Association

In this paper, the Bahadur representation of the empirical and Bernstein polynomials estimators of the quantile function based on associated sequences are investigated. The rate of approximation depends on the rate of decay in covariances, and it converges to the optimal rate observed under independence when the covariances quickly approach zero. As an application, we establish a Berry-Esseen bound with the rate \(O(n^{-1/3})\) assuming polynomial decay of covariances. All these results are established under fairly general conditions on the underlying distributions. Additionally, we perform Monte Carlo simulations to evaluate the finite sample performance of the suggested estimators, utilizing an associated and non-mixing model.

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来源期刊
CiteScore
1.70
自引率
0.00%
发文量
58
审稿时长
6-12 weeks
期刊介绍: Methodology and Computing in Applied Probability will publish high quality research and review articles in the areas of applied probability that emphasize methodology and computing. Of special interest are articles in important areas of applications that include detailed case studies. Applied probability is a broad research area that is of interest to many scientists in diverse disciplines including: anthropology, biology, communication theory, economics, epidemiology, finance, linguistics, meteorology, operations research, psychology, quality control, reliability theory, sociology and statistics. The following alphabetical listing of topics of interest to the journal is not intended to be exclusive but to demonstrate the editorial policy of attracting papers which represent a broad range of interests: -Algorithms- Approximations- Asymptotic Approximations & Expansions- Combinatorial & Geometric Probability- Communication Networks- Extreme Value Theory- Finance- Image Analysis- Inequalities- Information Theory- Mathematical Physics- Molecular Biology- Monte Carlo Methods- Order Statistics- Queuing Theory- Reliability Theory- Stochastic Processes
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