On the Choice of Difference Sequence in a Unified Framework for Variance Estimation in Nonparametric Regression

Wenlin Dai, Tiejun Tong, Lixing Zhu

Research output: Contribution to journalArticlepeer-review

3 Scopus citations

Abstract

Difference-based methods do not require estimating the mean function in nonparametric regression and are therefore popular in practice. In this paper, we propose a unified framework for variance estimation that combines the linear regression method with the higher-order difference estimators systematically. The unified framework has greatly enriched the existing literature on variance estimation that includes most existing estimators as special cases. More importantly, the unified framework has also provided a smart way to solve the challenging difference sequence selection problem that remains a long-standing controversial issue in nonparametric regression for several decades. Using both theory and simulations, we recommend to use the ordinary difference sequence in the unified framework, no matter if the sample size is small or if the signal-to-noise ratio is large. Finally, to cater for the demands of the application, we have developed a unified R package, named VarED, that integrates the existing difference-based estimators and the unified estimators in nonparametric regression and have made it freely available in the R statistical program http://cran.r-project.org/web/packages/.
Original languageEnglish (US)
Pages (from-to)455-468
Number of pages14
JournalStatistical Science
Volume32
Issue number3
DOIs
StatePublished - Sep 1 2017

Fingerprint

Dive into the research topics of 'On the Choice of Difference Sequence in a Unified Framework for Variance Estimation in Nonparametric Regression'. Together they form a unique fingerprint.

Cite this