A least squares method for variance estimation in heteroscedastic nonparametric regression

Yuejin Zhou, Yebin Cheng, Tiejun TONG*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

2 Citations (Scopus)

Abstract

Interest in variance estimation in nonparametric regression has grown greatly in the past several decades. Among the existing methods, the least squares estimator in Tong and Wang (2005) is shown to have nice statistical properties and is also easy to implement. Nevertheless, their method only applies to regression models with homoscedastic errors. In this paper, we propose two least squares estimators for the error variance in heteroscedastic nonparametric regression: the intercept estimator and the slope estimator. Both estimators are shown to be consistent and their asymptotic properties are investigated. Finally, we demonstrate through simulation studies that the proposed estimators perform better than the existing competitor in various settings.

Original languageEnglish
Article number585146
JournalJournal of Applied Mathematics
Volume2014
DOIs
Publication statusPublished - 2014

Scopus Subject Areas

  • Applied Mathematics

Fingerprint

Dive into the research topics of 'A least squares method for variance estimation in heteroscedastic nonparametric regression'. Together they form a unique fingerprint.

Cite this