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

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

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引用次数: 0

摘要

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。

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

查看原文本刊更多论文

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

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.

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来源期刊

Annals of Data Science Decision Sciences-Statistics, Probability and Uncertainty

CiteScore

6.50

自引率

0.00%

发文量

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.