Convergence rates for average square errors for kernel smoothing estimators
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Publication:2720137
DOI10.1080/10485250108832850zbMath0979.62032OpenAlexW2037488327MaRDI QIDQ2720137
Publication date: 18 February 2002
Published in: Journal of Nonparametric Statistics (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1080/10485250108832850
Related Items (3)
Nonparametric least squares estimation in derivative families ⋮ Central limit theorem for quadratic errors of Nadaraya-Watson regression estimator under dependence ⋮ The central limit theorem for degenerate variableU-statistics under dependence
Cites Work
- Central limit theorem for integrated square error of multivariate nonparametric density estimators
- Integrated square error properties of kernel estimators of regression functions
- Extent to which least-squares cross-validation minimises integrated square error in nonparametric density estimation
- Optimal bandwidth selection in nonparametric regression function estimation
- Random approximations to some measures of accuracy in nonparametric curve estimation
- A comparison of cross-validation techniques in density estimation
- Central limit theorems for quadratic errors of nonparametric estimators
- Asymptotic behaviors of some measures of accuracy in nonparametric curve estimation with dependent observations
- Local linear regression smoothers and their minimax efficiencies
- A Brief Survey of Bandwidth Selection for Density Estimation
- Loss and risk in smoothing parameter selection
- How Far Are Automatically Chosen Regression Smoothing Parameters From Their Optimum?
- Cross-Validation of Multivariate Densities
- Visual Error Criteria for Qualitative Smoothing
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