Empirical Bayes Estimates for a 2-Way Cross-Classified Additive Model

TL;DR

This paper introduces an empirical Bayes method for estimating cell means in unbalanced two-way additive models, optimizing hyperparameters via unbiased risk estimates, and demonstrating superior performance over traditional BLUP in simulations and real data.

Contribution

It develops a scalable, data-driven empirical Bayes estimator for unbalanced two-way models, improving upon the traditional BLUP approach.

Findings

The proposed estimator is asymptotically optimal.

It outperforms BLUP in unbalanced designs.

The method handles missing data effectively.

Abstract

We develop an empirical Bayes procedure for estimating the cell means in an unbalanced, two-way additive model with fixed effects. We employ a hierarchical model, which reflects exchangeability of the effects within treatment and within block but not necessarily between them, as suggested before by Lindley and Smith (1972). The hyperparameters of this hierarchical model, instead of considered fixed, are to be substituted with data-dependent values in such a way that the point risk of the empirical Bayes estimator is small. Our method chooses the hyperparameters by minimizing an unbiased risk estimate and is shown to be asymptotically optimal for the estimation problem defined above. The usual empirical Best Linear Unbiased Predictor (BLUP) is shown to be substantially different from the proposed method in the unbalanced case and therefore performs sub-optimally. Our estimator is implemented through a computationally tractable algorithm that is scalable to work under large designs. The case of missing cell observations is treated as well. We demonstrate the advantages of our method over the BLUP estimator through simulations and in a real data example, where we estimate average nitrate levels in water sources based on their locations and the time of the day.