References
- Statistical Science v.6 Choosing a kernel regression estimator(with comment) Chu, C. K.;Marron, J. S.
- Journal of the American Statistical Association v.87 Design-adaptive nonparametric regression Fan, J.
- Journal of the American Statistical Association v.86 A flexible and fast method for automatic smoothing Gasser, T.;Kneip, A.;Kohler, W.
- Smoothing Techniques for Curve Estimation Kernel estimation of regression function Gasser, T.;Muller, H. G.;T. Gasser(ed.);M. Rosenblatt(ed.)
- Scandinavian Journal of Statistics v.11 Estimating regression function and their derivatives by the kernel method Gasser, T.;Muller, H. G.
- Journal of the Royal Statistical Society, Ser. B v.47 Kernels for nonparametric curve estimation Gasser, T.;Muller, H. G.;Mammitzsch, V.
- Biometrika v.73 Residual variance and residual pattern in nonlinear regression Gasser, T.;Sroka, L.;Jennen-Steinmetz, C.
- Journal of the American Statistical Association v.83 How far are automatically chosen regression paremeters from their optimun?(with discussion) Hardle, W.;Hall, P.;Marron, J. S.
- Journal of the American Statistical Association v.87 Regression smoothing parameters that are not far from their optimum Hardle, W.;Hall, P.;Marron, J. S.
- Journal of the American Statistical Association v.89 Versions of kernel-type regression estimators Jones, M. C.;Davies, S. J.;Park, B.U.
- Theory of probability and Its Application v.9 On estimating regression Nadaraya, E. A.
- Journal of the Royal Statistical Society, Ser. B v.34 Non-parametric function fitting Priestley, M. B.;Chao, M. T.
- Sankhya, Ser. A v.26 Smooth regression analysis Watson, G. S.