Department of Biostatistics Seminar/Workshop Series

Inference for high-dimensional regression

Ali Shojaie, PhD, Associate Professor, Department of Biostatistics, University of Washington

Regularized estimation techniques, such as ridge and lasso, are widely used in high-dimensional regression to obtain more reliable parameter estimates and improve prediction accuracy. However, valid inference for these procedures remains challenging due to their inherent bias. In this talk, we revisit the variable selection effect of lasso and present a general framework for asymptotically valid inference using lasso. Time permitting, we will also discuss how external information can be incorporated in order to improve the power of high-dimensional inference.