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正态分布变异系数的贝叶斯样本量确定

Bayesian sample size determination for coefficient of variation of normal distribution.

作者信息

Ali Sajid, Waheed Mariyam, Shah Ismail, Raza Syed Muhammad Muslim

机构信息

Department of Statistics, Quaid-i-Azam University, Islamabad, Pakistan.

Department of Economics and Statistics, Dr Hasan Murad School of Management Sciences, University of Management and Technology, Lahore, Pakistan.

出版信息

J Appl Stat. 2023 Apr 4;51(7):1271-1286. doi: 10.1080/02664763.2023.2197571. eCollection 2024.

Abstract

Sample size determination is an active area of research in statistics. Generally, Bayesian methods provide relatively smaller sample sizes than the classical techniques, particularly average length criterion is more conventional and gives relatively small sample sizes under the given constraints. The objective of this study is to utilize major Bayesian sample size determination techniques for the coefficient of variation of normal distribution and assess their performance by comparing the results with the freqentist approach. To this end, we noticed that the average coverage criterion is the one that provides relatively smaller sample sizes than the worst outcome criterion. By comparing with the existing frequentist studies, we show that a smaller sample size is required in Bayesian methods to achieve the same efficiency.

摘要

样本量确定是统计学中一个活跃的研究领域。一般来说,贝叶斯方法提供的样本量比经典技术相对更小,特别是平均长度准则更为常用,并且在给定约束条件下给出的样本量相对较小。本研究的目的是利用主要的贝叶斯样本量确定技术来处理正态分布的变异系数,并通过与频率主义方法的结果进行比较来评估它们的性能。为此,我们注意到平均覆盖率准则比最坏结果准则提供的样本量相对更小。通过与现有的频率主义研究进行比较,我们表明在贝叶斯方法中需要较小的样本量来达到相同的效率。

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