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Smoothing parameter selection in nonparametric regression using an improved Akaike information criterion. (English) Zbl 0909.62039
Summary: Many different methods have been proposed to construct nonparametric estimates of a smooth regression function, including local polynomial, (convolution) kernel and smoothing spline estimators. Each of these estimators uses a smoothing parameter to control the amount of smoothing performed on a given data set. In this paper an improved version of a criterion based on the Akaike information criterion (AIC), termed $\text{AIC}_c$, is derived and examined as a way to choose the smoothing parameter. Unlike plug-in methods, $\text{AIC}_c$ can be used to choose smoothing parameters for any linear smoother, including local quadratic and smoothing spline estimators. The use of $\text{AIC}_c$ avoids the large variability and tendency to undersmooth (compared with the actual minimizer of average squared error) seen when other `classical’ approaches (such as generalized cross-validation or the AIC) are used to choose the smoothing parameter. Monte Carlo simulations demonstrate that the $\text{AIC}_c$-based smoothing parameter is competitive with a plug-in method (assuming that one exists) when the plug-in method works well but also performs well when the plug-in approach fails or is unavailable.

62G07Density estimation
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