主讲人：Hong Yili，Associate Professor of Virginia Tech, USA
主讲人介绍：Yili Hong received a BS in statistics in 2004 from University of Science and Technology of China. He received his MS in statistics in 2005 and PhD in statistics in 2009 from Iowa State University. He is currently an Associate Professor in the Department of Statistics at Virginia Tech. His research mainly focuses on machine learning and engineering applications, statistical reliability, survival analysis and spatial data analysis. His research has been published in top journals such as Technometrics, JQT, Annals of Applied Statistics, JASA, IEEE Transactions on Reliability, and Quality Engineering. He is one of the recipients of the 2011 DuPont Young Professor Award. He is an associate editor for Technometrics and JQT. He is a co-guest editor for a special issue on big data in reliability for JQT. He is an elected member of International Statistical Institute.
内容介绍：We use a spatial Poisson regression model to link the spatial disease counts and environmental and economic variables, and develop a spatial variable selection procedure to effectively identify important factors by using an adaptive elastic net penalty. The proposed methods can automatically select important covariates, while adjusting for possible spatial correlations of disease counts. The performance of the proposed method is studied and compared with existing methods via a comprehensive simulation study. We apply the developed variable selection methods to the Virginia Lyme disease data and identify important variables that are new to the literature.