# Meta-analysis of heterogeneous data: integrative sparse regression in high-dimensions

arXiv: https://arxiv.org/abs/1912.11928 · HTML: https://mlsanity.com/paper/1912.11928/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 Dec 2019.

## Built on by (most cited first, 1 of 1)

- [Federated Online Learning for Heterogeneous Multisource Streaming Data](https://mlsanity.com/paper/2508.06652/) (2025)

## Abstract (arXiv)

We consider the task of meta-analysis in high-dimensional settings in which the data sources are similar but non-identical. To borrow strength across such heterogeneous datasets, we introduce a global parameter that emphasizes interpretability and statistical efficiency in the presence of heterogeneity. We also propose a one-shot estimator of the global parameter that preserves the anonymity of the data sources and converges at a rate that depends on the size of the combined dataset. For high-dimensional linear model settings, we demonstrate the superiority of our identification restrictions in adapting to a previously seen data distribution as well as predicting for a new/unseen data distribution. Finally, we demonstrate the benefits of our approach on a large-scale drug treatment dataset involving several different cancer cell-lines.