在修正的 LINEX 损失加抽样成本条件下,对分布系列的规模参数进行两阶段和纯顺序最小风险点估计

IF 0.9 4区 数学 Q3 STATISTICS & PROBABILITY
Metrika Pub Date : 2024-09-12 DOI:10.1007/s00184-024-00973-1
Neeraj Joshi, Sudeep R. Bapat, Raghu Nandan Sengupta
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引用次数: 0

摘要

在本研究中,我们提出了两阶段和纯顺序方法,用于估算摩尔和毕利卡姆寿命分布系列的规模参数(参见摩尔和毕利卡姆在 IEEE Trans Reliabil 27:64-67, 1978 年的文章)。我们在最小风险点估计设置下提出了我们的方法,即考虑修正的 LINEX 损失函数和非线性采样成本。我们研究了与我们的停止规则相关的一些有趣的精确分布特性。我们还使用 Weibull 分布(Moore 和 Bilikam 系列的特例)进行了模拟分析,以检验我们的两阶段程序和纯序列程序的性能。最后,我们提供了 COVID-19 的真实数据集,并使用 Weibull 模型对其进行了分析,以支持我们提出的两阶段方法的实用性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Two-stage and purely sequential minimum risk point estimation of the scale parameter of a family of distributions under modified LINEX loss plus sampling cost

Two-stage and purely sequential minimum risk point estimation of the scale parameter of a family of distributions under modified LINEX loss plus sampling cost

In this research, we present two-stage and purely sequential methodologies for estimating the scale parameter of the Moore and Bilikam family of lifetime distributions (see Moore and Bilikam in IEEE Trans Reliabil 27:64–67, 1978). We propose our methodologies under the minimum risk point estimation setup, whereby we consider the modified LINEX loss function plus non linear sampling cost. We study some interesting exact distributional properties associated with our stopping rules. We also present simulation analyses using Weibull distribution (special case of the Moore and Bilikam family) to check the performance of our two-stage and purely sequential procedures. Finally, we provide a real data set from COVID-19 and analyze it using the Weibull model in support of the practical utility of our proposed two-stage methodology.

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来源期刊
Metrika
Metrika 数学-统计学与概率论
CiteScore
1.50
自引率
14.30%
发文量
39
审稿时长
6-12 weeks
期刊介绍: Metrika is an international journal for theoretical and applied statistics. Metrika publishes original research papers in the field of mathematical statistics and statistical methods. Great importance is attached to new developments in theoretical statistics, statistical modeling and to actual innovative applicability of the proposed statistical methods and results. Topics of interest include, without being limited to, multivariate analysis, high dimensional statistics and nonparametric statistics; categorical data analysis and latent variable models; reliability, lifetime data analysis and statistics in engineering sciences.
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