- Volume 4 Issue 1
A data-adaptive order selection procedure is proposed for local polynomial nonparametric regression. For each given polynomial order, bias and variance are estimated and the adaptive polynomial order that has the smallest estimated mean squared error is selected locally at each location point. To estimate mean squared error, empirical bias estimate of Ruppert (1995) and local polynomial variance estimate of Ruppert, Wand, Wand, Holst and Hossjer (1995) are used. Since the proposed method does not require fitting polynomial model of order higher than the model order, it is simpler than the order selection method proposed by Fan and Gijbels (1995b).
- Korean Journal of Applied Statistics v.7 An adaptive bandwidth selection algorithm in nonparametric regression Cha, K-J.;Lee, S-W.
- Journal of the Royal Statistical Society(Series B) v.57 Data-driven bandwidth selection in local polynomial fitting: variable bandwidth and spatial adaptation Fan, J.;Gijbels, I.
- Journal of Computational and Graphical Statistics v.4 Adaptive order polynomial fitting: bandwidth robustification and bias reduction Fan, J.;Gijbels, I.
- Local Polynomial Modelling and Its Applications Fan, J.;Gijbels, I.
- Applied Nonparametric Regression Hardle, W.
- Empirical-bias bandwidths for local polynomial nonparametric regression and density estimation, manuscript Ruppert, D.
- Journal of the American Statistical Association v.90 An effective bandwidth selector for local least squares regression Ruppert, D.;Sheather, S. J.;Wand, M. P.
- Local polynomial variance function estimation, manuscript Ruppert, D.;Wand, M. P.;Holst, U.;Hossjer, O.
- Kernel Smoothing Wand, M. P.;Jones, M. C.