  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.
