Multi-Goal Prior Selection: A Way to Reconcile Bayesian and Classical Approaches for Random Effects Models

The two-level normal hierarchical model has played an important role in statistical theory and applications. In this article, we first introduce a general adjusted maximum likelihood method for estimating the unknown variance component of the model and the associated empirical best linear unbiased p...

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Bibliographic Details
Published inJournal of the American Statistical Association Vol. 116; no. 535; pp. 1487 - 1497
Main Authors Hirose, Masayo Y., Lahiri, Partha
Format Journal Article
LanguageEnglish
Published Alexandria Taylor & Francis 03.07.2021
Taylor & Francis Ltd
Subjects
Adjusted maximum likelihood method; Empirical Bayes; Empirical best linear unbiased prediction; Linear mixed model
Bayesian analysis
Empirical analysis
First time
Maximum likelihood method
Objectives
Random effects
Regression analysis
Statistical methods
Statistics
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Summary:The two-level normal hierarchical model has played an important role in statistical theory and applications. In this article, we first introduce a general adjusted maximum likelihood method for estimating the unknown variance component of the model and the associated empirical best linear unbiased predictor of the random effects. We then discuss a new idea for selecting prior for the hyperparameters. The prior, called a multi-goal prior, produces Bayesian solutions for hyperparmeters and random effects that match (in the higher order asymptotic sense) the corresponding classical solution in linear mixed model with respect to several properties. Moreover, we establish for the first time an analytical equivalence of the posterior variances under the proposed multi-goal prior and the corresponding parametric bootstrap second-order mean squared error estimates in the context of a random effects model.
ISSN:0162-1459
1537-274X
DOI:10.1080/01621459.2020.1737532