Sparse Recovery With Multiple Data Streams: A Sequential Adaptive Testing Approach

TL;DR

This paper introduces a sequential adaptive testing method called SMART for sparse recovery across multiple data streams, optimizing measurement efficiency while controlling error rates in large-scale scientific applications.

Contribution

It develops a novel decision-theoretic framework and the SMART procedure for multi-stage adaptive testing that pools information to reduce measurements and control errors.

Findings

SMART controls FPR and MDR effectively

Significant savings in total measurements achieved

Validated through large-scale A/B tests and screening applications

Abstract

Multistage design has been used in a wide range of scientific fields. By allocating sensing resources adaptively, one can effectively eliminate null locations and localize signals with a smaller study budget. We formulate a decision-theoretic framework for simultaneous multi-stage adaptive testing and study how to minimize the total number of measurements while meeting pre-specified constraints on both the false positive rate (FPR) and missed discovery rate (MDR). The new procedure, which effectively pools information across individual tests using a simultaneous multistage adaptive ranking and thresholding (SMART) approach, controls the error rates and leads to great savings in total study costs. Numerical studies confirm the effectiveness of SMART. The SMART procedure is illustrated through the analysis of large-scale A/B tests, high-throughput screening and image analysis.