Online Program Home
  My Program

Abstract Details

Activity Number: 129 - Quantile and Nonparametric Regression Models
Type: Contributed
Date/Time: Monday, July 31, 2017 : 8:30 AM to 10:20 AM
Sponsor: Section on Nonparametric Statistics
Abstract #324513 View Presentation
Title: Adaptive Estimation for Varying Coefficient Models
Author(s): Yixin Chen* and Qin Wang and Weixin Yao
Companies: Sanofi and Virginia Commonwealth University and Department of Statistics, University of California, Riverside
Keywords: Adaptive estimation ; EM algorithm ; Kernel smoothing ; Local maximum likelihood ; Varying coefficient models

A novel adaptive estimation is proposed for varying coefficient models. Unlike the traditional least squares based methods, the proposed approach can adapt to different error distributions. An efficient EM algorithm is provided to implement the proposed estimation. The asymptotic properties of the resulting estimator are established. Both simulation studies and real data examples are used to illustrate the finite sample performance of the new estimation procedure. The numerical results show that the gain of the new procedure over the least squares estimation can be quite substantial for non-Gaussian errors.

Authors who are presenting talks have a * after their name.

Back to the full JSM 2017 program

Copyright © American Statistical Association