Improved Small Domain Estimation via Compromise Regression Weights

TL;DR

This paper introduces a robust, data-driven method for small domain estimation that combines model-based and observed predictors to improve accuracy under model misspecification.

Contribution

It proposes a new class of compromise regression weights that adaptively balance bias and variance, enhancing small domain estimates when the regression model may be misspecified.

Findings

The compromise predictor (CBP) improves estimation robustness.

The method effectively balances bias and variance.

Application to gait speed estimation demonstrates practical utility.

Abstract

Shrinkage estimates of small domain parameters typically utilize a combination of a noisy "direct" estimate that only uses data from a specific small domain and a more stable regression estimate. When the regression model is misspecified, estimation performance for the noisier domains can suffer due to substantial shrinkage towards a poorly estimated regression surface. In this paper, we introduce a new class of robust, empirically-driven regression weights that target estimation of the small domain means under potential misspecification of the global regression model. Our regression weights are a convex combination of the model-based weights associated with the best linear unbiased predictor (BLUP) and those associated with the observed best predictor (OBP). The compromise parameter in this convex combination is found by minimizing a novel, unbiased estimate of the mean-squared prediction error for the small domain means, and we label the associated small domain estimates the "compromise best predictor", or CBP. Using a data-adaptive mixture for the regression weights enables the CBP to possess the robustness of the OBP while retaining the main advantages of the EBLUP whenever the regression model is correct. We demonstrate the use of the CBP in an application estimating gait speed in older adults.