Divide-and-shrink: An efficient and heterogeneity-agnostic approach for transfer estimation

Thomas M. Menino Convention & Exhibition Center 

Knowledge transfer across data sources holds great promise for improving the estimation of target population parameters by leveraging the growing availability of data from different sources. However, the effectiveness of knowledge transfer is often challenged by the complex and pervasive heterogeneity between data sources and the lack of access to individual-level data. This paper proposes the divide-and-shrink (dShrink) method, a transfer estimation method that estimates target population parameters in a closed form using summary statistics from a target population and an external source population while accounting for population heterogeneity. dShrink is guaranteed to improve the estimator using the target population in the expected quadratic error under arbitrary population heterogeneity. Notably, it is model-free, requires no user-specified tuning parameters, robust to various types of heterogeneity between data sources, and applies to a broad range of parameter estimation problems. Simulations and real data analyses demonstrate the superior performance of the dShrink estimator and its potential as a robust tool for transfer estimation.

Combining information

Data integration

Efficiency

Population heterogeneity

Robustness

Shrinkage estimators 

Royal Statistical Society