一类随机变量的完全f矩收敛及其相关的统计应用

IF 0.5 4区数学 Q4 STATISTICS & PROBABILITY

Stochastic Models Pub Date : 2023-10-11 DOI:10.1080/15326349.2023.2253278

Liangxue Li, Xuejun Wang, Chen Yi

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引用次数: 0

摘要

摘要在本文中，我们建立了一类随机变量满足rosenthal型极大不等式和具有平均支配变量的弱平均支配条件的完全f矩收敛性。作为推论，也得到了一类随机变量的完全矩收敛性和完全收敛性。此外，给出了主要结果在非参数回归模型中的应用。最后，通过有限样本的数值模拟验证了理论结果的有效性。关键词:完全一致性完全f矩收敛非参数回归模型rosenthal型极大不等式感谢编辑和匿名审稿人仔细阅读稿件并提出宝贵意见，帮助改进本文的早期版本。披露声明作者未报告潜在的利益冲突。附加信息国家社会科学基金项目(22BTJ059)资助。

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Complete f -moment convergence for a class of random variables with related statistical applications

Abstract.In this article, we establish the complete f-moment convergence for a class of random variables satisfying a Rosenthal-type maximal inequality and a weak mean dominating condition with a mean dominating variable. As corollaries, the complete moment convergence and complete convergence for a class of random variables are also obtained. In addition, an application of main results to nonparametric regression models is provided. Finally, we provide a numerical simulation to verify the validity of our theoretical results based on finite samples.Keywords: Complete consistencycomplete f-moment convergencenonparametric regression modelsRosenthal-type maximal inequalityMSC:: 60F1562G20 AcknowledgmentsThe authors are most grateful to the Editor and anonymous referee for carefully reading the manuscript and valuable suggestions which helped in improving an earlier version of this paper.Disclosure statementNo potential conflict of interest was reported by the authors.Additional informationFunding Supported by the National Social Science Foundation of China (22BTJ059).

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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.