a7977c0412c24410VgnVCM100000c2b1d38dRCRDapproved/UMICH/stats/Home/News & Events/Statistics SeminarDepartment Seminar Series: Runze Li, Feature Selection for Varying Coefficient Models With Ultrahigh Dimensional Covariates###@###(Fri, 18 Apr 2014)Department Seminar Series: Runze Li, Feature Selection for Varying Coefficient Models With Ultrahigh Dimensional Covariates###@###(Fri, 18 Apr 2014)340 West Hallstats1397835000000139783500000011:30 AM<p style=" font-size: 11.0pt; margin-bottom: 10.0pt; margin-left: 0in; margin-top: 0in; margin-right: 0in; line-height: 115%; font-family: Calibri,sans-serif;">This talk is concerned with feature screening and variable selection for varying coefficient models with ultrahigh dimensional covariates. We propose a new feature screening procedure for these models based on conditional correlation coefficient. We systematically study the theoretical properties of the proposed procedure, and establish their sure screening property and the ranking consistency. To enhance the finite sample performance of the proposed procedure, we further develop an iterative feature screening procedure. Monte Carlo simulation studies were conducted to examine the performance of the proposed procedures. In practice, we advocate a two-stage approach for varying coefficient models. The two stage approach consists of (a) reducing the ultrahigh dimensionality by using the proposed procedure and (b) applying regularization methods for dimension-reduced varying coefficient models to make statistical inferences on the coefficient functions. We illustrate the proposed two-stage approach by a real data example.</p>Nlorieannbzuniga139603224307377977c0412c24410VgnVCM100000c2b1d38d____once11112newnewRunze Li, PhD., Professor, Department of Statistics, Penn State Universityhttp://sites.stat.psu.edu/~rli/