行m负相关随机变量阵列的完全f矩收敛及其统计应用

IF 0.5 4区数学 Q4 STATISTICS & PROBABILITY

Stochastic Models Pub Date : 2023-01-09 DOI:10.1080/15326349.2022.2149554

Miaomiao Wang, Min Wang, Xuejun Wang, Fei Zhang

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引用次数: 2

摘要

摘要在本文中，我们研究了在一些一般条件下，逐列m负相关随机变量阵列的完全f矩收敛性。文中得到的结果对已有的一些结果进行了扩展和改进。作为主要结果的应用，我们给出了基于m-负相关误差的半参数回归模型中估计量的完全一致性。我们在有限样本的基础上进行了一些数值模拟，以验证理论结果的有效性。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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Complete f-moment convergence for arrays of rowwise m-negatively associated random variables and its statistical applications

Abstract In this paper, we study the complete f-moment convergence for arrays of rowwise m-negatively associated random variables under some general conditions. The results obtained in the paper extend and improve some previous known ones. As an application of the main results, we present the complete consistency for the estimator in a semiparametric regression model based on m-negatively associated errors. We perform some numerical simulations to verify the validity of the theoretical results based on finite samples.

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来源期刊

Stochastic Models 数学-统计学与概率论

CiteScore

1.30

自引率

14.30%

发文量

审稿时长

>12 weeks

期刊介绍： Stochastic Models publishes papers discussing the theory and applications of probability as they arise in the modeling of phenomena in the natural sciences, social sciences and technology. It presents novel contributions to mathematical theory, using structural, analytical, algorithmic or experimental approaches. In an interdisciplinary context, it discusses practical applications of stochastic models to diverse areas such as biology, computer science, telecommunications modeling, inventories and dams, reliability, storage, queueing theory, mathematical finance and operations research.