混合截尾逆lmax分布:在生存数据中的应用

IF 1.6 Q1 STATISTICS & PROBABILITY
A. Yadav, S. Singh, U. Singh
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引用次数: 25

摘要

本文提出了一种估计方法来估计逆Lomax分布的未知参数、可靠性和危险函数。导出了存在混合滤波方案时最大似然估计量和贝叶斯估计量的数学表达式。在大多数情况下,可以看到参数的极大似然估计量和贝叶斯估计量不是以显式形式出现的。因此,牛顿-拉夫森(N-R)方法被用于绘制参数的最大似然估计。利用马尔可夫链蒙特卡罗(MCMC)技术,在Jeffrey的非信息先验条件下得到了形状和规模的贝叶斯估计量。此外,我们还构建了基于最大似然估计(MLEs)的95%渐近置信区间和基于MCMC样本的最高后验密度(HPD)可信区间。最后,使用两个数据集来演示所提出的方法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
On Hybrid Censored Inverse Lomax Distribution: Application to the Survival Data
In this paper, we proposed the estimation procedures to estimate the unknown parameters, reliability and hazard functions of Inverse Lomax distribution. The mathematical expressions for maximum likelihood and Bayes estimators are derived in presence of hybrid censoring scheme. In most of the cases, it has been seen that maximum likelihood and Bayes estimators of the parameters are not appear in explicit form. Hence, Newton-Raphson (N-R) method has been used to draw the maximum likelihood estimates of the parameters. The Bayes estimators are obtained under Jeffrey's non-informative prior for both shape  and scale using Markov Chain Monte Carlo (MCMC) technique. Further, we have also constructed the 95% asymptotic confidence interval based on maximum likelihood estimates (MLEs) and highest posterior density (HPD) credible intervals based on MCMC samples. Finally, two data sets have been used to demonstrate the proposed methodology.
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来源期刊
Statistica
Statistica STATISTICS & PROBABILITY-
CiteScore
1.70
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0.00%
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10 weeks
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