BayesSRW: Bayesian Sampling and Re-weighting approach for variance reduction

TL;DR

BayesSRW introduces a Bayesian sampling and re-weighting method that optimally allocates samples across groups to reduce variance and improve estimation accuracy in resource-constrained settings.

Contribution

The paper presents a novel variance reduction sampling strategy based on Cauchy-Schwarz inequality and extends it with a Bayesian two-stage approach for better variance estimation.

Findings

Effective variance reduction demonstrated in simulations

Improved estimation precision in diverse applications

Resource-efficient sampling strategy outperforms traditional methods

Abstract

In this paper, we address the challenge of sampling in scenarios where limited resources prevent exhaustive measurement across all subjects. We consider a setting where samples are drawn from multiple groups, each following a distribution with unknown mean and variance parameters. We introduce a novel sampling strategy, motivated simply by Cauchy-Schwarz inequality, which minimizes the variance of the population mean estimator by allocating samples proportionally to both the group size and the standard deviation. This approach improves the efficiency of sampling by focusing resources on groups with greater variability, thereby enhancing the precision of the overall estimate. Additionally, we extend our method to a two-stage sampling procedure in a Bayes approach, named BayesSRW, where a preliminary stage is used to estimate the variance, which then informs the optimal allocation of the remaining sampling budget. Through simulation examples, we demonstrate the effectiveness of our approach in reducing estimation uncertainty and providing more reliable insights in applications ranging from user experience surveys to high-dimensional peptide array studies.