for this workshop

## Inference in high dimensional regression

at the

American Institute of Mathematics, San Jose, California

organized by

Peter Buehlmann, Andrea Montanari, and Jonathan Taylor

This workshop, sponsored by AIM and the NSF, 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.

The workshop will differ from typical conferences in some regards. Participants will be invited to suggest open problems and questions before the workshop begins, and these will be posted on the workshop website. These include specific problems on which there is hope of making some progress during the workshop, as well as more ambitious problems which may influence the future activity of the field. Lectures at the workshop will be focused on familiarizing the participants with the background material leading up to specific problems, and the schedule will include discussion and parallel working sessions.

The deadline to apply for support to participate in this workshop has passed.

For more information email *workshops@aimath.org*