An iterative procedure for calculating minimum generalised cross validation smoothing splines

A Generalized Approximate Cross Validation for Smoothing Splines with Non-gaussian Data

In this paper, we propose a Generalized Approximate Cross Validation (GACV) function for estimating the smoothing parameter in the penalized log likelihood regression problem with non-Gaussian data. This GACV is obtained by, first, obtaining an approximation to the leaving-out-one function based on the negative log likelihood, and then, in a step reminiscent of that used to get from leaving-out...

Generalised Cross Validation for Noise-Free Data

An iterative algorithm for minimum cross entropy thresholding

A fast iterative method is derived for minimum cross entropy thresholding using a one-point iteration scheme. Simulations performed using synthetic generated histograms and a real image show the speed advantage and the accuracy of the iterated version. q 1998 Elsevier Science B.V. All rights reserved.

Contour reconstruction using recursive smoothing splines - Algorithms and experimental validation

In this paper, a recursive smoothing spline approach for contour reconstruction is studied and evaluated. Periodic smoothing splines are used by a robot to approximate the contour of encountered obstacles in the environment. The splines are generated through minimizing a cost function subject to constraints imposed by a linear control system and accuracy is improved iteratively using a recursiv...

Equivalent Kernels for Smoothing Splines

In the study of smoothing spline estimators, some convolution-kernellike properties of the Green’s function for an appropriate boundary value problem, depending on the design density, are needed. For the uniform density, the Green’s function can be computed more or less explicitly. Then, integral equation methods are brought to bear to establish the kernel-like properties of said Green’s functi...