Bayes estimates of variance components in mixed linear model

IF 1.1 4区数学 Q4 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Journal of Statistical Computation and Simulation Pub Date : 2023-10-24 DOI:10.1080/00949655.2023.2273369

Jie Jiang, Tian He, Lichun Wang

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引用次数: 0

Abstract

AbstractThis paper proves that in mixed linear model, the analysis of variance estimation (ANOVAE), the minimum norm quadratic unbiased estimation (MINQUE), the spectral decomposition estimation (SDE) and the restricted maximum likelihood estimation (RMLE) of variance components are the same under some conditions. Based on this result, we construct a linear Bayes estimation (LBE) for the parameter vector consisting of variance components and establish its superiorities. Numerical computations and an illustration show that the LBE is comparable to Lindley's approximation, Tierney and Kadane's approximation and the usual Bayes estimation (UBE) obtained by the MCMC method and easy to use as well.Keywords: Mixed linear modelvariance componentslinear Bayes procedure AcknowledgmentsWe would like to thank the Editor and reviewers for the comments and suggestions, which have improved the presentation and quality of the paper.Disclosure statementNo potential conflict of interest was reported by the author(s).Additional informationFundingSupported by NNSF of China (11371051)

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混合线性模型中方差分量的贝叶斯估计

摘要本文证明了在混合线性模型中，方差分析估计(ANOVAE)、最小范数二次无偏估计(MINQUE)、谱分解估计(SDE)和方差分量的限制性极大似然估计(RMLE)在一定条件下是相同的。在此基础上，构造了由方差分量组成的参数向量的线性贝叶斯估计(LBE)，并确定了其优越性。数值计算和实例表明，该方法可与Lindley近似、Tierney和Kadane近似以及常用的MCMC方法得到的贝叶斯估计(UBE)相媲美，且易于使用。关键字:混合线性模型方差成分线性贝叶斯过程致谢我们要感谢编辑和审稿人的意见和建议，他们改善了论文的表达和质量。披露声明作者未报告潜在的利益冲突。中国国家自然科学基金资助项目(11371051)

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来源期刊

Journal of Statistical Computation and Simulation 数学-计算机：跨学科应用

CiteScore

2.30

自引率

8.30%

发文量

156

审稿时长

4-8 weeks

期刊介绍： Journal of Statistical Computation and Simulation ( JSCS ) publishes significant and original work in areas of statistics which are related to or dependent upon the computer. Fields covered include computer algorithms related to probability or statistics, studies in statistical inference by means of simulation techniques, and implementation of interactive statistical systems. JSCS does not consider applications of statistics to other fields, except as illustrations of the use of the original statistics presented. Accepted papers should ideally appeal to a wide audience of statisticians and provoke real applications of theoretical constructions.