A Variable Selection Technique for Detecting Climate Change Attribution

• Activity Number: 602
• Type: Contributed
• Date/Time: Wednesday, August 7, 2013 : 2:00 PM to 3:50 PM
• Sponsor: Section on Statistics and the Environment
• Author(s): Siddhartha Nandy*+ and Chae Young Lim and Tapabrata Maiti
• Companies: Michigan State University and Michigan State and Michigan State University
• Keywords: Climate change and attribution ; Spectral radiance change ; Variable selection ; Spatial additive models ; Adaptive group LASSO

Abstract

There is a considerable interest of developing sound quantitative and computationally feasible statistical tools suitable for analyzing spectral radiance change data to monitor the climate change and attributions. In this context, we developed a variable selection technique, specifically adaptive group LASSO type of selection, followed by a semiparametric additive model. We validate the method by simulation studies and applying to simulated spectral radiance measurement data for climate change. Theoretical validity will also be discussed.