NeurIPS 2018poster19 citations
Derivative Estimation in Random Design
Abstract
We propose a nonparametric derivative estimation method for random design without having to estimate the regression function. The method is based on a variance-reducing linear combination of symmetric difference quotients. First, we discuss the special case of uniform random design and establish the estimator’s asymptotic properties. Secondly, we generalize these results for any distribution of the dependent variable and compare the proposed estimator with popular estimators for derivative estimation such as local polynomial regression and smoothing splines.
BibTeX
@inproceedings{NEURIPS2018_db29450c,
author = {Liu, Yu and De Brabanter, Kris},
booktitle = {Advances in Neural Information Processing Systems},
editor = {S. Bengio and H. Wallach and H. Larochelle and K. Grauman and N. Cesa-Bianchi and R. Garnett},
pages = {},
publisher = {Curran Associates, Inc.},
title = {Derivative Estimation in Random Design},
url = {https://proceedings.neurips.cc/paper_files/paper/2018/file/db29450c3f5e97f97846693611f98c15-Paper.pdf},
volume = {31},
year = {2018}
}