an:03872479
Zbl 0547.62024
Silverman, B. W.
Spline smoothing: The equivalent variable kernel method
EN
Ann. Stat. 12, 898-916 (1984).
00149160
1984
j
62G05 65D10 62J02 65C99 46E35
variable kernel; roughness penalty; penalized maximum likelihood; curve estimation; density estimation; Sobolev spaces; cubic spline estimator; kernel approach; spline-smoothing methods; approximation of the weight functions; hat matrix
The cubic spline estimator of the regression curve is related with the existence of a weight function which enables to perform a nonparametric estimation of the function. The relation between this method and the kernel approach is fixed under suitable conditions with the aims of giving an intuitive insight into spline-smoothing methods.
The main result is that for a sequence of probability distribution functions \(F_ n\), the weight function corresponding to a design point is similar to a specific kernel function k if n is sufficiently large, the smoothing parameter \(\lambda\) is small and the design point t is not near the boundaries of the interval (a,b) on which the sequence is defined. k is centred in t with bandwidth \((\lambda /F'(t))^{1/4}\), where \(F=\lim_{n\to\infty }F_ n\) is an absolutely continuous distribution function on (a,b).
This result is given in theorem A under assumptions related with the existence of F, the boundness of its first and second derivatives and restrictions on how fast \(\lambda\) should tend to zero. From three lemmas and two propositions given in Section 4 the theorem is obtained. As the approximation of the weight function is not good when t is close to the boundaries of (a,b) a solution is stated in theorem B of Section 5.
Illustrations of the performance of the approximation of the weight functions are given. Some applications are derived in connection with the hat matrix and the estimation of a density function.
C.N.Bouza