Online Program Home
  My Program

Abstract Details

Activity Number: 403 - Selected Topics on Hypothesis Testing and Statistical Inference
Type: Contributed
Date/Time: Tuesday, August 1, 2017 : 2:00 PM to 3:50 PM
Sponsor: IMS
Abstract #322788 View Presentation
Title: Nonparametric Inference via Bootstrapping the Debiased Estimator
Author(s): Yen-Chi Chen*
Companies: University of Washington
Keywords: kernel density estimator ; nonparametric regression ; confidence set ; bootstrap
Abstract:

In this talk, we propose to construct confidence bands by bootstrapping the debiased kernel density estimator (for density estimation) and the debiased local polynomial regression estimator (for regression analysis). The idea of using a debiased estimator was first introduced in Calonico et al. (2015), where they construct a confidence interval of the density function (and regression function) at a given point by explicitly estimating stochastic variations. We extend their ideas and propose a bootstrap approach for constructing confidence bands that is uniform for every point in the support. We prove that the resulting bootstrap confidence band is asymptotically valid and is compatible with most tuning parameter selection approaches, such as the rule of thumb and cross-validation. We further generalize our method to confidence sets of density level sets and inverse regression problems. Simulation studies confirm the validity of the proposed confidence bands/sets.


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

Back to the full JSM 2017 program

 
 
Copyright © American Statistical Association