Statistics and Data Science Seminar
Prof. Weixing Song
Kansas State University
Nonparametric Regression With Errors in Partial Variables
Abstract: An estimation procedure is proposed for a nonparametric regression in which some covariates are measured with errors and some are not. The procedure combines the ordinary and deconvolution kernel estimation techniques. It is shown that the optimal local and global convergence rates, as well as the uniform convergence rate over a class of joint distributions of the response and the covariates, depend on the tail behavior of the characteristic functions of the measurement error distributions. Examples are given to show the general applicability of the proposed methodology, and finite sample performance is evaluated by some numerical simulation studies.
Wednesday November 6, 2013 at 4:00 PM in SEO 636