#
Inference in high dimensional regression

January 20 to January 23, 2015
at the

American Institute of Mathematics,
San Jose, California

organized by

Peter Buehlmann,
Andrea Montanari,
and Jonathan Taylor

## Original Announcement

This workshop will be devoted to exploring recent methodological and theoretical
advances in inference for high-dimensional statistical models.
Classical statistical theory analyzed in great detail the case in which the
number of parameters is much smaller than the
number of samples. Methods testing statistical hypotheses in this
context lie at the foundation of all empirical sciences.
Modern datasets are exceptionally fine-grained and this poses a number of new
challenges to the
classical framework. Technically, this is the so-called high-dimensional regime
whereby the number of parameters is of the same order, or even
larger than the number of samples. It is crucial, in this case, to
make some structural assumptions regarding the unknown parameter
vector -- e.g. imposing sparsity constraints.

While the estimation problem in high-dimension has been object of considerable
amount of
research over the last 10 years, much less is known about hypothesis
testing or confidence intervals. The question is of great practical
relevance, and this has spurred several methodological proposals over the last
year,
in the context of regression modeling.

This workshop aims at bringing together researchers in the field and promote
collaboration and
discussion around the problem of performing hypothesis
testing or computing confidence intervals in high-dimensional
statistical problems.

## Material from the workshop

A list of participants.
The workshop schedule.

A report on the workshop activities.

Papers arising from the workshop: