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

Abstract

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.

keyword:

• Adjusted maximum likelihood method; Empirical Bayes; Empirical best linear unbiased prediction; Linear mixed model

Acknowledgments

We thank Professor Shuhei Mano, anonymous associate editor, and referees for reading an earlier version of the article carefully and offering a number of constructive suggestions, which led to a significant improvement of our article.

Funding

The first and second authors’ research was partially supported by JSPS KAKENHI grant number 18K12758 and U.S. National Science Foundation grants SES-1534413 and SES-1758808, respectively.