非线性自回归模型中误差密度估计器的渐近结果

IF 0.6 4区数学 Q4 STATISTICS & PROBABILITY

Journal of the Korean Statistical Society Pub Date : 2024-02-13 DOI:10.1007/s42952-024-00258-3

Shipeng Wu, Wenzhi Yang, Min Gao, Hongyan Fang

{"title":"非线性自回归模型中误差密度估计器的渐近结果","authors":"Shipeng Wu, Wenzhi Yang, Min Gao, Hongyan Fang","doi":"10.1007/s42952-024-00258-3","DOIUrl":null,"url":null,"abstract":"This paper considers the asymptotic properties of the error density estimator in the nonlinear autoregressive models with \\(\\alpha\\)-mixing errors. The asymptotic distribution and uniform convergence rate for the kernel density estimator of error density function are obtained. Last, some simulations of histograms, confidence intervals and mean integrated square errors are illustrated, which agree with our theoretical results.","PeriodicalId":49992,"journal":{"name":"Journal of the Korean Statistical Society","volume":"29 1","pages":""},"PeriodicalIF":0.6000,"publicationDate":"2024-02-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Asymptotic results of error density estimator in nonlinear autoregressive models\",\"authors\":\"Shipeng Wu, Wenzhi Yang, Min Gao, Hongyan Fang\",\"doi\":\"10.1007/s42952-024-00258-3\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This paper considers the asymptotic properties of the error density estimator in the nonlinear autoregressive models with \\\\(\\\\alpha\\\\)-mixing errors. The asymptotic distribution and uniform convergence rate for the kernel density estimator of error density function are obtained. Last, some simulations of histograms, confidence intervals and mean integrated square errors are illustrated, which agree with our theoretical results.\",\"PeriodicalId\":49992,\"journal\":{\"name\":\"Journal of the Korean Statistical Society\",\"volume\":\"29 1\",\"pages\":\"\"},\"PeriodicalIF\":0.6000,\"publicationDate\":\"2024-02-13\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of the Korean Statistical Society\",\"FirstCategoryId\":\"100\",\"ListUrlMain\":\"https://doi.org/10.1007/s42952-024-00258-3\",\"RegionNum\":4,\"RegionCategory\":\"数学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q4\",\"JCRName\":\"STATISTICS & PROBABILITY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of the Korean Statistical Society","FirstCategoryId":"100","ListUrlMain":"https://doi.org/10.1007/s42952-024-00258-3","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: 0

摘要

本文研究了具有混杂误差的非线性自回归模型中误差密度估计器的渐近特性。本文给出了误差密度函数核密度估计器的渐近分布和均匀收敛率。最后，对直方图、置信区间和平均积分平方误差进行了一些模拟说明，这些结果与我们的理论结果一致。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

Asymptotic results of error density estimator in nonlinear autoregressive models

查看原文本刊更多论文

Asymptotic results of error density estimator in nonlinear autoregressive models

This paper considers the asymptotic properties of the error density estimator in the nonlinear autoregressive models with \(\alpha\)-mixing errors. The asymptotic distribution and uniform convergence rate for the kernel density estimator of error density function are obtained. Last, some simulations of histograms, confidence intervals and mean integrated square errors are illustrated, which agree with our theoretical results.

求助全文

通过发布文献求助，成功后即可免费获取论文全文。去求助

来源期刊

Journal of the Korean Statistical Society 数学-统计学与概率论

CiteScore

1.30

自引率

0.00%

发文量

审稿时长

3 months

期刊介绍： The Journal of the Korean Statistical Society publishes research articles that make original contributions to the theory and methodology of statistics and probability. It also welcomes papers on innovative applications of statistical methodology, as well as papers that give an overview of current topic of statistical research with judgements about promising directions for future work. The journal welcomes contributions from all countries.