On Progressively Censored Generalized X-Exponential Distribution: (Non) Bayesian Estimation with an Application to Bladder Cancer Data

Q1 Decision Sciences Annals of Data Science Pub Date : 2023-06-15 DOI:10.1007/s40745-023-00477-1
Kousik Maiti, Suchandan Kayal, Aditi Kar Gangopadhyay
{"title":"On Progressively Censored Generalized X-Exponential Distribution: (Non) Bayesian Estimation with an Application to Bladder Cancer Data","authors":"Kousik Maiti,&nbsp;Suchandan Kayal,&nbsp;Aditi Kar Gangopadhyay","doi":"10.1007/s40745-023-00477-1","DOIUrl":null,"url":null,"abstract":"<div><p>This article addresses estimation of the parameters and reliability characteristics of a generalized <i>X</i>-Exponential distribution based on the progressive type-II censored sample. The maximum likelihood estimates (MLEs) are obtained. The uniqueness and existence of the MLEs are studied. The Bayes estimates are obtained under squared error and entropy loss functions. For computation of the Bayes estimates, Markov Chain Monte Carlo method is used. Bootstrap-<i>t</i> and bootstrap-<i>p</i> methods are used to compute the interval estimates. Further, a simulation study is performed to compare the performance of the proposed estimates. Finally, a real-life dataset is considered and analysed for illustrative purposes.</p></div>","PeriodicalId":36280,"journal":{"name":"Annals of Data Science","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2023-06-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Annals of Data Science","FirstCategoryId":"1085","ListUrlMain":"https://link.springer.com/article/10.1007/s40745-023-00477-1","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"Decision Sciences","Score":null,"Total":0}
引用次数: 0

Abstract

This article addresses estimation of the parameters and reliability characteristics of a generalized X-Exponential distribution based on the progressive type-II censored sample. The maximum likelihood estimates (MLEs) are obtained. The uniqueness and existence of the MLEs are studied. The Bayes estimates are obtained under squared error and entropy loss functions. For computation of the Bayes estimates, Markov Chain Monte Carlo method is used. Bootstrap-t and bootstrap-p methods are used to compute the interval estimates. Further, a simulation study is performed to compare the performance of the proposed estimates. Finally, a real-life dataset is considered and analysed for illustrative purposes.

Abstract Image

查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
关于渐进截尾广义X指数分布:(非)贝叶斯估计及其在癌症数据中的应用
本文探讨了基于渐进式 II 型删减样本的广义 X 指数分布的参数估计和可靠性特征。得到了最大似然估计值(MLE)。研究了 MLE 的唯一性和存在性。在平方误差和熵损失函数下获得贝叶斯估计值。在计算贝叶斯估计值时,使用了马尔可夫链蒙特卡罗方法。使用 Bootstrap-t 和 Bootstrap-p 方法计算区间估计值。此外,还进行了模拟研究,以比较建议的估计值的性能。最后,考虑并分析了现实生活中的一个数据集,以作说明。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 去求助
来源期刊
Annals of Data Science
Annals of Data Science Decision Sciences-Statistics, Probability and Uncertainty
CiteScore
6.50
自引率
0.00%
发文量
93
期刊介绍: Annals of Data Science (ADS) publishes cutting-edge research findings, experimental results and case studies of data science. Although Data Science is regarded as an interdisciplinary field of using mathematics, statistics, databases, data mining, high-performance computing, knowledge management and virtualization to discover knowledge from Big Data, it should have its own scientific contents, such as axioms, laws and rules, which are fundamentally important for experts in different fields to explore their own interests from Big Data. ADS encourages contributors to address such challenging problems at this exchange platform. At present, how to discover knowledge from heterogeneous data under Big Data environment needs to be addressed.     ADS is a series of volumes edited by either the editorial office or guest editors. Guest editors will be responsible for call-for-papers and the review process for high-quality contributions in their volumes.
期刊最新文献
Non-negative Sparse Matrix Factorization for Soft Clustering of Territory Risk Analysis Kernel Method for Estimating Matusita Overlapping Coefficient Using Numerical Approximations Maximum Likelihood Estimation for Generalized Inflated Power Series Distributions Farm-Level Smart Crop Recommendation Framework Using Machine Learning Reaction Function for Financial Market Reacting to Events or Information
×
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
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 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 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1