Variance estimation in nonparametric regression with jump discontinuities

Wenlin Dai, Tiejun TONG*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

2 Citations (Scopus)

Abstract

Variance estimation is an important topic in nonparametric regression. In this paper, we propose a pairwise regression method for estimating the residual variance. Specifically, we regress the squared difference between observations on the squared distance between design points, and then estimate the residual variance as the intercept. Unlike most existing difference-based estimators that require a smooth regression function, our method applies to regression models with jump discontinuities. Our method also applies to the situations where the design points are unequally spaced. Finally, we conduct extensive simulation studies to evaluate the finite-sample performance of the proposed method and compare it with some existing competitors.

Original languageEnglish
Pages (from-to)530-545
Number of pages16
JournalJournal of Applied Statistics
Volume41
Issue number3
DOIs
Publication statusPublished - Mar 2014

Scopus Subject Areas

  • Statistics and Probability
  • Statistics, Probability and Uncertainty

User-Defined Keywords

  • difference-based estimator
  • jump point
  • non-uniform design
  • nonparametric regression
  • pairwise regression
  • residual variance

Fingerprint

Dive into the research topics of 'Variance estimation in nonparametric regression with jump discontinuities'. Together they form a unique fingerprint.

Cite this