Estimation of change-points in a nonparametric regression function through kernel density estimation

DOI10.1080/03610929408831432zbMath0823.62034OpenAlexW2016342072MaRDI QIDQ4839315

No author found.

Publication date: 17 July 1995

Published in: Communications in Statistics - Theory and Methods (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1080/03610929408831432

zbMATH Keywords

asymptotic normalitycross-validationkernel density estimatorcentral limit theoremsnonparametric regressionstrong convergence rates

Mathematics Subject Classification ID

Density estimation (62G07) Asymptotic properties of nonparametric inference (62G20)

Related Items (3)

Data-Driven Discontinuity Detection in Derivatives of a Regression Function ⋮ Bootstrap test for change-points in nonparametric regression ⋮ Change-point problems: bibliography and review

Cites Work

• Unnamed Item
• Unnamed Item
• Bandwidth choice for nonparametric regression
• Optimal bandwidth selection in nonparametric regression function estimation
• Estimation of heteroscedasticity in regression analysis
• Change-points in nonparametric regression analysis
• Kernel-type estimators of jump points and values of a regression function
• Local linear regression smoothers and their minimax efficiencies
• Smoothing with Split Linear Fits
• Asymptotically optimal difference-based estimation of variance in nonparametric regression
• Double smoothing for kernelestimators in nonparametric regression
• On variance estimation in nonparametric regression
• Residual variance and residual pattern in nonlinear regression
• Detection of the number, locations and magnitudes of jumps
• Design-adaptive Nonparametric Regression

This page was built for publication: Estimation of change-points in a nonparametric regression function through kernel density estimation