Nonparametric least squares estimation of a regression function
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Publication:3795063
DOI10.1080/02331888808802107zbMath0649.62034OpenAlexW2067772355MaRDI QIDQ3795063
Publication date: 1988
Published in: Statistics (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1080/02331888808802107
mean square errorleast squares methodmean integrated square errorstrong uniform convergencefinite variancezero meanconvergence rate of MISEnonparametric function fittingSufficient conditions for consistency
Related Items (10)
Nonparametric trigonometric orthogonal regression estimation ⋮ Learning shape sensitive descriptors for classifying functional data ⋮ Least squares orthogonal polynomial regression estimation for irregular design ⋮ On finite-sample properties of adaptive least squares regression estimates ⋮ The asymptotic average squared error for polynomial regression ⋮ Nonparametric multiple function fitting ⋮ Risk of estimators for Sobol' sensitivity indices based on metamodels ⋮ On function recovery by neural networks based on orthogonal expansions ⋮ Trigonometric regression estimation for observations with additive and multiplicative errors ⋮ On least squares discrete Fourier analysis of unequally spaced data
Cites Work
- Kernel estimates of functions and their derivatives with applications
- Asymptotic properties of kernel estimates of a regression function
- Contributions to the theory of nonparametric regression, with application to system identification
- Optimal global rates of convergence for nonparametric regression
- Nonparametric regression: An up–to–date bibliography
- On system identification by nonparametric function fitting
- On the L 1 convergence of kernel estimators of regression functions with applications in discrimination
- Curve Estimates
- Nonparametric estimation of a regression function
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