Spatially adaptive smoothing splines

Abstract

We use a reproducing kernel Hilbert space representation to derive the smoothing spline solution when the smoothness penalty is a function λ( t ) of the design space t , thereby allowing the model to adapt to various degrees of smoothness in the structure of the data. We propose a convenient form for the smoothness penalty function and discuss computational algorithms for automatic curve fitting using a generalised crossvalidation measure.