B1329
Title: Variable selection in kernel regression using measurement error selection likelihoods
Authors: Yichao Wu - The University of Illinois at Chicago (United States) [presenting]
Abstract: We present a nonparametric shrinkage and selection estimator via a measurement error selection likelihood approach recently proposed. The Measurement Error Kernel Regression Operator (MEKRO) has the same form as the Nadaraya-Watson kernel estimator, but optimizes a measurement error model selection likelihood to estimate the kernel bandwidths. Much like LASSOor COSSO solution paths, MEKRO results in solution paths depending on a tuning parameter that controls shrinkage and selection via a bound on the harmonic mean of the pseudo-measurement error standard deviations.
