North Carolina State University
Variable selection via measurement error model selection likelihoods
Abstract: The measurement error model selection likelihood was proposed in Stefanski, Wu and White (2014) to conduct variable selection. It provides a new perspective on variable selection. The first part of my talk will be a review of the measurement error model selection likelihoods. The second part is an extension to nonparametric variable selection in kernel regression. If time permits, I will briefly present a related flexible nonparametric variable screening method that we have proposed recently.
Friday January 13, 2017 at 3:00 PM in SEO 636