广义熵损失函数下毛刺III型分布的贝叶斯移位点估计

U. Srivastava, H. Kumar
{"title":"广义熵损失函数下毛刺III型分布的贝叶斯移位点估计","authors":"U. Srivastava, H. Kumar","doi":"10.37622/gjpam/18.2.2022.465-477","DOIUrl":null,"url":null,"abstract":"This paper considers a mean shift with an unknown shift point in a process Burr type III sequence and estimates the unknown shift point (change point) by the method of Bayesian Estimation Procedure. Pre-shift and post-shift means are estimated concurrently with the change point. When the underlying process overestimate or underestimate the mean shift in process we use asymmetric Loss function as General entropy Loss Function in order to estimate the mean shift. A Simulation Study is done by using R software.","PeriodicalId":198465,"journal":{"name":"Global Journal of Pure and Applied Mathematics","volume":"35 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2022-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Bayesian Shift Point Estimation of Burr Type III Distribution under General Entropy Loss Function\",\"authors\":\"U. Srivastava, H. Kumar\",\"doi\":\"10.37622/gjpam/18.2.2022.465-477\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This paper considers a mean shift with an unknown shift point in a process Burr type III sequence and estimates the unknown shift point (change point) by the method of Bayesian Estimation Procedure. Pre-shift and post-shift means are estimated concurrently with the change point. When the underlying process overestimate or underestimate the mean shift in process we use asymmetric Loss function as General entropy Loss Function in order to estimate the mean shift. A Simulation Study is done by using R software.\",\"PeriodicalId\":198465,\"journal\":{\"name\":\"Global Journal of Pure and Applied Mathematics\",\"volume\":\"35 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2022-06-30\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Global Journal of Pure and Applied Mathematics\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.37622/gjpam/18.2.2022.465-477\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Global Journal of Pure and Applied Mathematics","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.37622/gjpam/18.2.2022.465-477","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0

摘要

本文研究了一类过程Burrⅲ型序列中具有未知移位点的均值移位,并利用贝叶斯估计方法对未知移位点(变化点)进行了估计。位移前均值和位移后均值与变化点同时估计。当基础过程高估或低估过程中的平均位移时,我们使用非对称损失函数作为一般熵损失函数来估计平均位移。利用R软件进行了仿真研究。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Bayesian Shift Point Estimation of Burr Type III Distribution under General Entropy Loss Function
This paper considers a mean shift with an unknown shift point in a process Burr type III sequence and estimates the unknown shift point (change point) by the method of Bayesian Estimation Procedure. Pre-shift and post-shift means are estimated concurrently with the change point. When the underlying process overestimate or underestimate the mean shift in process we use asymmetric Loss function as General entropy Loss Function in order to estimate the mean shift. A Simulation Study is done by using R software.
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
自引率
0.00%
发文量
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信