Inference as optimization in a multivariate linear regression with Laplace priors

Event Sponsor: 
LANS Informal Seminar
Start Date: 
Feb 15 2010 - 3:00pm to 4:00pm
Building 240, Rm 1404-1405
Argonne National Laboratory
Peter Carbonetto
Speaker(s) Title: 
Postdoctoral Researcher, Dept. of Human Genetics, University of Chicago

The Lasso---the maximum likelihood estimator for a linear regression model with a penalty on the absolute values of the regression coefficients---is an important statistical method for a wide range of fields. The Lasso plays a key role in genetic association studies, where the goal is to discover genetic variants that make us more predisposed to certain diseases. One major appeal of the Lasso is that there are efficient optimization algorithms for computing the solution. The drawback is that the Lasso is unable to report its confidence in a solution, and this is vital for genetic association studies. I explain how to pose the full inference problem as an optimization problem with semidefinite constraints, and how formulating it in this way leads to a natural convex relaxation. This is work in progress.

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