Transfer learning on stratified data: joint estimation transferred from strata

This paper studies the target model with the help of auxiliary models from different but possibly re-lated groups. Inspired by transfer learning, we propose a method called joint estimation transferred from strata (JETS). To obtain a sparse solution, JETS constructs a penalized framework combining a term that penalizes the target model and an additional term that penalizes the differences between auxiliary mod-els and the target model. In this way, JETS overcomes the challenge caused by the limited samples in high-dimensional study, and obtains stable and accurate estimates regardless of whether auxiliary sam-ples contain noisy information. We demonstrate that this method enjoys the computational advantage of the traditional methods such as the lasso. During simulations and applications, the proposed method is compared with several existing methods and JETS outperforms others.& COPY; 2023 Elsevier Ltd. All rights reserved.

Automated summary

This paper proposes a method called JETS that utilizes auxiliary models from different groups to estimate the target model. By constructing a penalized framework that combines penalties for the target model and the differences between auxiliary models and the target model, JETS overcomes the challenge of limited samples in high-dimensional studies and obtains stable and accurate estimates, regardless of noisy information in the auxiliary samples.