Nonparametric Error Variance Estimation in Regression: A Review

Irène Gijbels ()
Additional contact information
Irène Gijbels: University of Leuven (KU Leuven), Department of Mathematics

Chapter Chapter 19 in Asymptotic and Methodological Statistics, 2026, pp 381-401 from Springer

Abstract: Abstract Nonparametric estimation of a conditional mean function in regression has received a lot of attention. Apart from the mean function, also estimation of the conditional variance function (the error variance) is of interest. This paper reviews the main approaches in estimation of the error variance in regression: difference-based and residual-based procedures. Which specific statistical methods are appropriate very much depends on whether the design is fixed or random, and the regression context is homoscedastic or heteroscedastic. We present the review keeping these issues in mind, and discuss some recent contributions in error variance estimation. For simplicity of presentation we restrict to kernel methods in this paper.

Date: 2026
References: Add references at CitEc
Citations:

There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-3-032-07178-1_19

Ordering information: This item can be ordered from
http://www.springer.com/9783032071781

DOI: 10.1007/978-3-032-07178-1_19

Access Statistics for this chapter

More chapters in Springer Books from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().