Empirical Bayes Estimation with Side Information: A Nonparametric Integrative Tweedie Approach

TL;DR

This paper introduces a nonparametric integrative Tweedie approach within an empirical Bayes framework that uses side information to improve the estimation of normal means, with theoretical guarantees and demonstrated superior performance.

Contribution

It develops a novel nonparametric method that incorporates auxiliary data into empirical Bayes estimation using convex optimization, providing theoretical risk analysis and practical improvements.

Findings

NIT improves estimation accuracy with side information.

Theoretical risk bounds and convergence rates are established.

Numerical experiments show NIT outperforms existing methods.

Abstract

We investigate the problem of compound estimation of normal means while accounting for the presence of side information. Leveraging the empirical Bayes framework, we develop a nonparametric integrative Tweedie (NIT) approach that incorporates structural knowledge encoded in multivariate auxiliary data to enhance the precision of compound estimation. Our approach employs convex optimization tools to estimate the gradient of the log-density directly, enabling the incorporation of structural constraints. We conduct theoretical analyses of the asymptotic risk of NIT and establish the rate at which NIT converges to the oracle estimator. As the dimension of the auxiliary data increases, we accurately quantify the improvements in estimation risk and the associated deterioration in convergence rate. The numerical performance of NIT is illustrated through the analysis of both simulated and real data, demonstrating its superiority over existing methods.