{"title":"一种利用独立样本用插入式方法消除干扰参数的方法","authors":"George Tzavelas","doi":"10.1007/s10463-025-00927-y","DOIUrl":null,"url":null,"abstract":"<div><p>The estimation of the structural parameter in the presence of a nuisance parameter is an old and challenging problem. The usual estimating method is the plug-in likelihood method, using the same data set for estimating both the structural as well as the nuisance parameters. The aim of this paper is to provide an optimal estimating function for the estimation of the parameter of interest using the plug-in method, when an estimator for the nuisance parameter is available independent of the sample used to estimate the structural parameter.</p></div>","PeriodicalId":55511,"journal":{"name":"Annals of the Institute of Statistical Mathematics","volume":"77 4","pages":"627 - 648"},"PeriodicalIF":0.6000,"publicationDate":"2025-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"A way of eliminating a nuisance parameter with the plug-in method utilizing an independent sample\",\"authors\":\"George Tzavelas\",\"doi\":\"10.1007/s10463-025-00927-y\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p>The estimation of the structural parameter in the presence of a nuisance parameter is an old and challenging problem. The usual estimating method is the plug-in likelihood method, using the same data set for estimating both the structural as well as the nuisance parameters. The aim of this paper is to provide an optimal estimating function for the estimation of the parameter of interest using the plug-in method, when an estimator for the nuisance parameter is available independent of the sample used to estimate the structural parameter.</p></div>\",\"PeriodicalId\":55511,\"journal\":{\"name\":\"Annals of the Institute of Statistical Mathematics\",\"volume\":\"77 4\",\"pages\":\"627 - 648\"},\"PeriodicalIF\":0.6000,\"publicationDate\":\"2025-04-15\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Annals of the Institute of Statistical Mathematics\",\"FirstCategoryId\":\"100\",\"ListUrlMain\":\"https://link.springer.com/article/10.1007/s10463-025-00927-y\",\"RegionNum\":4,\"RegionCategory\":\"数学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"STATISTICS & PROBABILITY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Annals of the Institute of Statistical Mathematics","FirstCategoryId":"100","ListUrlMain":"https://link.springer.com/article/10.1007/s10463-025-00927-y","RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"STATISTICS & PROBABILITY","Score":null,"Total":0}
A way of eliminating a nuisance parameter with the plug-in method utilizing an independent sample
The estimation of the structural parameter in the presence of a nuisance parameter is an old and challenging problem. The usual estimating method is the plug-in likelihood method, using the same data set for estimating both the structural as well as the nuisance parameters. The aim of this paper is to provide an optimal estimating function for the estimation of the parameter of interest using the plug-in method, when an estimator for the nuisance parameter is available independent of the sample used to estimate the structural parameter.
期刊介绍:
Annals of the Institute of Statistical Mathematics (AISM) aims to provide a forum for open communication among statisticians, and to contribute to the advancement of statistics as a science to enable humans to handle information in order to cope with uncertainties. It publishes high-quality papers that shed new light on the theoretical, computational and/or methodological aspects of statistical science. Emphasis is placed on (a) development of new methodologies motivated by real data, (b) development of unifying theories, and (c) analysis and improvement of existing methodologies and theories.