Smooth supersaturated models

TL;DR

This paper introduces a novel method for constructing smooth interpolators by extending polynomial bases and minimizing smoothness measures, offering an alternative to splines with near-optimal smoothing in multiple dimensions.

Contribution

It presents a new algebraic approach to create smooth interpolators through basis extension and smoothness minimization, applicable in any dimension and region, with demonstrated connections to splines.

Findings

Achieves arbitrarily close to optimal smoothing in multiple dimensions

Provides a simple alternative to spline models

Benchmarking shows competitive performance against kriging methods

Abstract

In areas such as kernel smoothing and non-parametric regression there is emphasis on smooth interpolation and smooth statistical models. Splines are known to have optimal smoothness properties in one and higher dimensions. It is shown, with special attention to polynomial models, that smooth interpolators can be constructed by first extending the monomial basis and then minimising a measure of smoothness with respect to the free parameters in the extended basis. Algebraic methods are a help in choosing the extended basis which can also be found as a saturated basis for an extended experimental design with dummy design points. One can get arbitrarily close to optimal smoothing for any dimension and over any region, giving a simple alternative models of spline type. The relationship to splines is shown in one and two dimensions. A case study is given which includes benchmarking against kriging methods.