{"title":"从似然方程推导出的加权指数族新闭式点估计器","authors":"Roberto Vila, Eduardo Nakano, Helton Saulo","doi":"10.1002/sta4.723","DOIUrl":null,"url":null,"abstract":"In this paper, we propose and investigate closed‐form point estimators for a weighted exponential family. We also develop a bias‐reduced version of these proposed closed‐form estimators through bootstrap methods. Estimators are assessed using a Monte Carlo simulation, revealing favourable results for the proposed bootstrap bias‐reduced estimators. We illustrate the proposed methodology with the use of two real data sets.","PeriodicalId":0,"journal":{"name":"","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-08-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Novel Closed‐Form Point Estimators for a Weighted Exponential Family Derived From Likelihood Equations\",\"authors\":\"Roberto Vila, Eduardo Nakano, Helton Saulo\",\"doi\":\"10.1002/sta4.723\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In this paper, we propose and investigate closed‐form point estimators for a weighted exponential family. We also develop a bias‐reduced version of these proposed closed‐form estimators through bootstrap methods. Estimators are assessed using a Monte Carlo simulation, revealing favourable results for the proposed bootstrap bias‐reduced estimators. We illustrate the proposed methodology with the use of two real data sets.\",\"PeriodicalId\":0,\"journal\":{\"name\":\"\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0,\"publicationDate\":\"2024-08-28\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"\",\"FirstCategoryId\":\"100\",\"ListUrlMain\":\"https://doi.org/10.1002/sta4.723\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"","FirstCategoryId":"100","ListUrlMain":"https://doi.org/10.1002/sta4.723","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Novel Closed‐Form Point Estimators for a Weighted Exponential Family Derived From Likelihood Equations
In this paper, we propose and investigate closed‐form point estimators for a weighted exponential family. We also develop a bias‐reduced version of these proposed closed‐form estimators through bootstrap methods. Estimators are assessed using a Monte Carlo simulation, revealing favourable results for the proposed bootstrap bias‐reduced estimators. We illustrate the proposed methodology with the use of two real data sets.