{"title":"Complete f-moment convergence for arrays of rowwise m-negatively associated random variables and its statistical applications","authors":"Miaomiao Wang, Min Wang, Xuejun Wang, Fei Zhang","doi":"10.1080/15326349.2022.2149554","DOIUrl":null,"url":null,"abstract":"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.","PeriodicalId":21970,"journal":{"name":"Stochastic Models","volume":"39 1","pages":"632 - 661"},"PeriodicalIF":0.5000,"publicationDate":"2023-01-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Stochastic Models","FirstCategoryId":"100","ListUrlMain":"https://doi.org/10.1080/15326349.2022.2149554","RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"STATISTICS & PROBABILITY","Score":null,"Total":0}
引用次数: 2
Abstract
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.
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.
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