# Confidence Intervals for Maximin Effects in Inhomogeneous Large-Scale Data

arXiv: https://arxiv.org/abs/1502.07963 · HTML: https://mlsanity.com/paper/1502.07963/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Feb 2015.

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

- [Distributionally Robust Learning for Multi-source Unsupervised Domain Adaptation](https://mlsanity.com/paper/2309.02211/) (2023)

## Abstract (arXiv)

One challenge of large-scale data analysis is that the assumption of an identical distribution for all samples is often not realistic. An optimal linear regression might, for example, be markedly different for distinct groups of the data. Maximin effects have been proposed as a computationally attractive way to estimate effects that are common across all data without fitting a mixture distribution explicitly. So far just point estimators of the common maximin effects have been proposed in Meinshausen and B"uhlmann (2014). Here we propose asymptotically valid confidence regions for these effects.