  Personalized medicine has become an important part of medicine, for instance
predicting individual drug responses based on genomic information. However,
many current statistical methods are not tailored to this task, because they
overlook the individual heterogeneity of patients. In this paper, we look at
personalized medicine from a linear regression standpoint. We introduce an
alternative version of the ridge estimator and target individuals by
establishing a tuning parameter calibration scheme that minimizes prediction
errors of individual patients. In stark contrast, classical schemes such as
cross-validation minimize prediction errors only on average. We show that our
pipeline is optimal in terms of oracle inequalities, fast, and highly effective
both in simulations and on real data.
