In this paper, we develop a fast algorithm for a smoothing spline estimator in multivariate regression. To accomplish this, we employ general concepts associated with roughness penalty methods in conjunction with the theory of radial basis functions and reproducing kernel Hilbert spaces. It is shown that through the use of compactly supported radial basis functions it becomes possible to recove...

Smoothing splines are one of the most popular approaches to nonparametric regression. Wahba (1978,1983) showed that smoothing splines are also Bayes estimates and used the corresponding prior model to derive interval estimates for the regression function. Although the interval estimates work well on a global basis, they can have poor local properties. The source of this problem is the use of a ...

Nonparametric regression techniques such as spline smoothing and local tting depend implicitly on a parametric model. For instance, the cubic smoothing spline estimate of a regression function based on observations ti; Yi is the minimizer of P(Yi (ti))2 + R ( 00)2. Since R ( 00)2 is zero when is a line, the cubic smoothing spline estimate favors the parametric model (t) = 0+ 1t: Here we conside...

The author proposes some simple diagnostics for the assessment of the necessity of selected model terms in smoothing spline ANOVA models; the elimination of practically insignificant terms generally enhances the interpretability of the estimates, and sometimes may also have inferential implications. The diagnostics are derived from Kullback-Leibler geometry, and are illustrated in the settings ...

I discuss the production of low rank smoothers for d ≥ 1 dimensional data, which can be fitted by regression or penalized regression methods. The smoothers are constructed by a simple transformation and truncation of the basis that arises from the solution of the thinplate spline smoothing problem, and are optimal in the sense that the truncation is designed to result in the minimum possible pe...

Wepropose the generalized profilingmethod to estimate themultiple regression functions in the framework of penalized spline smoothing, where the regression functions and the smoothing parameter are estimated in two nested levels of optimization. The corresponding gradients and Hessian matrices are worked out analytically, using the Implicit Function Theorem if necessary, which leads to fast and...

This paper presents a method for incorporating anatomical NMR boundary side information into penalized maximumlikelihood (PML) emission image reconstructions. The NMR boundary is parameterized as a periodic spline curve of fixed order and number of knots that is known a priori. Maximum Likelihood (ME) estimation of the spline coefficients yields an “extracted” boundary, which is used to define ...

Smoothing splines are one of the most popular approaches to nonparametric regression. Wahba (J. Roy. Statist. Soc. Set. B 40 (1978) 364-372; 45 (1983) 133-150) showed that smoothing splines are also Bayes estimates and used the corresponding prior model to derive interval estimates for the regression function. Although the interval estimates work well on a global basis, they can have poor local...

schemes, it provides a uniform theoretical base for some other smoothing methods. It has shown that the elegant Gaussian smoothing (GS) is equivalent to the initial value problem of DS, and the widely-used Repeated Averaging (RA) is a special case of the explicit DS. This paper further proves that Spline smoothing (SS) is a special case of the explicit DS with a "convex corner cling" boundary c...

A spline function is a curve constructed from polynomial segments that are subject to conditions or continuity at their joints. In this paper, we shall present the algorithm of the cubic smoothing spline and we shall justify its use in estimating trends in time series. Considerable effort has been devoted over several decades to developing the mathematics of spline functions. Much of the intere...