WOD阵列加权和的完全f矩收敛与统计应用

IF 0.9 4区计算机科学 Q4 COMPUTER SCIENCE, CYBERNETICS

Kybernetika Pub Date : 2023-03-10 DOI:10.14736/kyb-2023-1-0001

Xi Chen, Xinran Tao, Xuejun Wang

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引用次数: 1

摘要

完全f矩收敛比完全收敛和完全矩收敛要普遍得多。在这项工作中，我们主要研究了广泛正交相关(WOD，简称WOD)数组加权和的完全f矩收敛性。在一些合适的条件下，得到了f矩完全收敛的一般结果，推广了文献中相应的结果。作为一个应用，我们建立了非参数回归模型中加权线性估计量的完全相合性。最后，通过仿真验证了基于有限样本的理论结果的数值性能。

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Complete f-moment convergence for weighted sums of WOD arrays with statistical applications

Complete f -moment convergence is much more general than complete convergence and complete moment convergence. In this work, we mainly investigate the complete f -moment convergence for weighted sums of widely orthant dependent (WOD, for short) arrays. A general result on Complete f -moment convergence is obtained under some suitable conditions, which generalizes the corresponding one in the literature. As an application, we establish the complete consistency for the weighted linear estimator in nonparametric regression models. Finally, some simulations are provided to show the numerical performance of theoretical results based on ﬁnite samples.

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来源期刊

Kybernetika 工程技术-计算机：控制论

CiteScore

1.30

自引率

20.00%

发文量

审稿时长

6 months

期刊介绍： Kybernetika is the bi-monthly international journal dedicated for rapid publication of high-quality, peer-reviewed research articles in fields covered by its title. The journal is published by Nakladatelství Academia, Centre of Administration and Operations of the Czech Academy of Sciences for the Institute of Information Theory and Automation of The Czech Academy of Sciences. Kybernetika traditionally publishes research results in the fields of Control Sciences, Information Sciences, Statistical Decision Making, Applied Probability Theory, Random Processes, Operations Research, Fuzziness and Uncertainty Theories, as well as in the topics closely related to the above fields.