主讲人： Dr. Jin (Gordon) Liu（耶鲁大学）
简介：In genome-wide association studies, penalization is an important approach for identifying genetic markers associated with disease. Motivated by the fact that there exists natural grouping structure in SNPs (single nucleotide polymorphism) and more importantly such groups are correlated, we propose a new penalization method for group variable selection which can properly accommodate the correlation between adjacent groups. This method is based on a combination of the group Lasso penalty and a quadratic penalty on difference of regression coefficients of adjacent groups. The new method is referred to as smoothed group Lasso, or SGL.