经典误差和伯克逊误差混合下线性变量误差模型的估计

IF 0.7 Q3 STATISTICS & PROBABILITY

Modern Stochastics-Theory and Applications Pub Date : 2021-01-01 DOI:10.15559/21-vmsta186

Mykyta Yakovliev, A. Kukush

{"title":"经典误差和伯克逊误差混合下线性变量误差模型的估计","authors":"Mykyta Yakovliev, A. Kukush","doi":"10.15559/21-vmsta186","DOIUrl":null,"url":null,"abstract":"A linear structural regression model is studied, where the covariate is observed with a mixture of the classical and Berkson measurement errors. Both variances of the classical and Berkson errors are assumed known. Without normality assumptions, consistent estimators of model parameters are constructed and conditions for their asymptotic normality are given. The estimators are divided into two asymptotically independent groups.","PeriodicalId":42685,"journal":{"name":"Modern Stochastics-Theory and Applications","volume":"89 1","pages":""},"PeriodicalIF":0.7000,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Estimation in a linear errors-in-variables model under a mixture of classical and Berkson errors\",\"authors\":\"Mykyta Yakovliev, A. Kukush\",\"doi\":\"10.15559/21-vmsta186\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"A linear structural regression model is studied, where the covariate is observed with a mixture of the classical and Berkson measurement errors. Both variances of the classical and Berkson errors are assumed known. Without normality assumptions, consistent estimators of model parameters are constructed and conditions for their asymptotic normality are given. The estimators are divided into two asymptotically independent groups.\",\"PeriodicalId\":42685,\"journal\":{\"name\":\"Modern Stochastics-Theory and Applications\",\"volume\":\"89 1\",\"pages\":\"\"},\"PeriodicalIF\":0.7000,\"publicationDate\":\"2021-01-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Modern Stochastics-Theory and Applications\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.15559/21-vmsta186\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"STATISTICS & PROBABILITY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Modern Stochastics-Theory and Applications","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.15559/21-vmsta186","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"STATISTICS & PROBABILITY","Score":null,"Total":0}

引用次数: 0

摘要

研究了一个线性结构回归模型，其中协变量是用经典和伯克森测量误差的混合来观察的。假设经典误差和伯克逊误差的方差都是已知的。在没有正态性假设的情况下，构造了模型参数的一致估计量，并给出了其渐近正态性的条件。估计量被分成两个渐近独立的群。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

查看原文本刊更多论文

Estimation in a linear errors-in-variables model under a mixture of classical and Berkson errors

A linear structural regression model is studied, where the covariate is observed with a mixture of the classical and Berkson measurement errors. Both variances of the classical and Berkson errors are assumed known. Without normality assumptions, consistent estimators of model parameters are constructed and conditions for their asymptotic normality are given. The estimators are divided into two asymptotically independent groups.

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

Modern Stochastics-Theory and Applications STATISTICS & PROBABILITY-

CiteScore

1.30

自引率

50.00%

发文量

审稿时长

10 weeks