Estimating the suspected larger of two normal means

IF 0.9 4区 数学 Q3 STATISTICS & PROBABILITY Metrika Pub Date : 2024-04-04 DOI:10.1007/s00184-024-00961-5
Courtney Drew, Éric Marchand
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Abstract

For \(X_1, X_2\) independently and normally distributed with means \(\theta _1\) and \(\theta _2\), variances \(\sigma ^2_1\) and \(\sigma ^2_2\), we consider Bayesian inference about \(\theta _1\) with the difference \(\theta _1-\theta _2\) being lower-bounded by an uncertain m. We obtain a class of minimax Bayes estimators of \(\theta _1\), based on a posterior distribution for \((\theta _1, \theta _2)^{\top }\) taking values on \(\mathbb {R}^2\), which dominate the unrestricted MLE under squared error loss for \(\theta _1-\theta _2 \ge 0\). We also construct and study an ad hoc credible set for \(\theta _1\) with approximate credibility \(1-\alpha \) and provide numerical evidence of its frequentist coverage probability closely matching the nominal credibility level. A spending function is incorporated which further increases the coverage.

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估计两个正态均值中较大的可疑值
对于 \(X_1, X_2\) 独立正态分布,具有均值 \(\theta _1\) 和 \(\theta _2\), 方差 \(\sigma ^2_1\) 和 \(\sigma ^2_2\)、我们考虑用贝叶斯推理来推断差值 \(\theta _1-\theta _2\) 被不确定的 m 限定。我们基于取值于 \(\mathbb {R}^2\) 的 \((\theta _1, \theta _2)^\{top }\) 的后验分布得到了一类 \(\theta _1\(\theta _1, \theta _2)^\{top }\) 的最小贝叶斯估计值,这些估计值在 \(\theta _1-\theta _2 \ge 0\) 的平方误差损失下支配了无限制的 MLE。我们还为 \(theta _1-\theta _2\ge 0\) 构造并研究了一个近似可信度为 \(1-\alpha \)的特设可信集,并提供了其频数覆盖概率与名义可信度密切匹配的数值证据。支出函数的加入进一步提高了覆盖率。
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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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