Grace Wahba

Spline Models for Observational Data

A completely automatic french curve: fitting spline functions by cross validation

The cross validation mean square error technique is used to determine the correct degree of smoothing, in fitting smoothing solines to discrete, noisy observations from some unk...

Spline Models for Observational Data.

This book serves well as an introduction into the more theoretical aspects of the use of spline models. It develops a theory and practice for the estimation of functions from no...

Smoothing noisy data with spline functions

Minimizing GCV/GML Scores with Multiple Smoothing Parameters via the Newton Method

The (modified) Newton method is adapted to optimize generalized cross validation (GCV) and generalized maximum likelihood (GML) scores with multiple smoothing parameters. The ma...

The Computation of Generalized Cross-Validation Functions Through Householder Tridiagonalization with Applications to the Fitting of Interaction Spline Models

An efficient algorithm for computing the GCV (generalized cross-validation) function for the general cross-validated regularization/smoothing problem is provided. This algorithm...

Partial and interaction spline models for the semiparametric estimation of functions of several variables

A partial spline model is a model for a response as a function of several variables, which is the sum of a smooth function of several variables and a parametric function of the ...