Complete f -moment convergence for m -asymptotic negatively associated random variables and related statistical applications

IF 0.8 4区数学 Q3 STATISTICS & PROBABILITY

Journal of Nonparametric Statistics Pub Date : 2023-11-09 DOI:10.1080/10485252.2023.2280004

Xuejun Wang, Xi Chen, Tien-Chung Hu, Andrei Volodin

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引用次数: 0

Abstract

AbstractIn this article, the complete f-moment convergence for m-asymptotic negatively associated random variables is investigated. As applications, we establish the strong consistency of the least square estimator in the simple linear errors-in-variables models and the complete consistency for estimator in the semiparametric regression model based on m-asymptotic negatively associated errors. We also give some simulations to assess the finite sample performance of the theoretical results.Keywords: m-Asymptotic negatively associated random variablescomplete f-moment convergenceconsistencyerrors-in-variables modelssemiparametric regression modelsMathematics Subject Classifications: 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 author(s).Additional informationFundingSupported by the National Social Science Foundation of China (22BTJ059).

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m -渐近负相关随机变量的完全f矩收敛及其统计应用

摘要本文研究了m-渐近负相关随机变量的完全f矩收敛性。作为应用，我们建立了简单线性变量误差模型中最小二乘估计量的强相合性和基于m-渐近负相关误差的半参数回归模型中估计量的完全相合性。我们还进行了一些仿真来评估理论结果的有限样本性能。关键词:m-渐近负相关随机变量完全f矩收敛一致性变量误差模型半参数回归模型数学学科分类:60F1562G20致谢作者非常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。

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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来源期刊

Journal of Nonparametric Statistics 数学-统计学与概率论

CiteScore

1.50

自引率

8.30%

发文量

审稿时长

6-12 weeks

期刊介绍： Journal of Nonparametric Statistics provides a medium for the publication of research and survey work in nonparametric statistics and related areas. The scope includes, but is not limited to the following topics: Nonparametric modeling, Nonparametric function estimation, Rank and other robust and distribution-free procedures, Resampling methods, Lack-of-fit testing, Multivariate analysis, Inference with high-dimensional data, Dimension reduction and variable selection, Methods for errors in variables, missing, censored, and other incomplete data structures, Inference of stochastic processes, Sample surveys, Time series analysis, Longitudinal and functional data analysis, Nonparametric Bayes methods and decision procedures, Semiparametric models and procedures, Statistical methods for imaging and tomography, Statistical inverse problems, Financial statistics and econometrics, Bioinformatics and comparative genomics, Statistical algorithms and machine learning. Both the theory and applications of nonparametric statistics are covered in the journal. Research applying nonparametric methods to medicine, engineering, technology, science and humanities is welcomed, provided the novelty and quality level are of the highest order. Authors are encouraged to submit supplementary technical arguments, computer code, data analysed in the paper or any additional information for online publication along with the published paper.