Error variance estimation via least squares for small sample nonparametric regression

DOI10.1016/J.JSPI.2012.02.050zbMath1244.62061OpenAlexW2013483747MaRDI QIDQ433760

Yung-Seop Lee, Chun Gun Park, Inyoung Kim

Publication date: 6 July 2012

Published in: Journal of Statistical Planning and Inference (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.jspi.2012.02.050

zbMATH Keywords

tablesleast squaresLipschitz conditiondifference-based estimatorresidual variance

Mathematics Subject Classification ID

Nonparametric regression and quantile regression (62G08) Linear regression; mixed models (62J05)

Related Items (6)

Variance estimation for semiparametric regression models by local averaging ⋮ Outlier detection using difference-based variance estimators in multiple regression ⋮ Robust difference-based outlier detection ⋮ Weighted linear regression models with fixed weights and spherical disturbances ⋮ On the choice of difference sequence in a unified framework for variance estimation in nonparametric regression ⋮ A least squares method for variance estimation in heteroscedastic nonparametric regression

Cites Work

• Unnamed Item
• Unnamed Item
• Bandwidth choice for nonparametric regression
• Asymptotically optimal difference-based estimation of variance in nonparametric regression
• Nonparametric estimation of residual variance revisited
• Estimating residual variance in nonparametric regression using least squares
• Residual variance and residual pattern in nonlinear regression
• Estimating the Variance In Nonparametric Regression—What is a Reasonable Choice?
• Fully Data-Driven Nonparametric Variance Estimators

This page was built for publication: Error variance estimation via least squares for small sample nonparametric regression