Abstract

this paper we consider alternative methods based on parametric maximum likelihood fits, using a radial basis function representation of the nonlinear map. A key part of the fitting procedure is model selection, or equivalently, reduction in dimensionality by selection of a subset of radial basis functions. The methodology is illustrated with two examples, one based on tropospheric ozone and the other on U.S. climate data. However a number of cautions are noted: there is no guarantee of uniqueness of the estimates and the evidence that more complicated models result in improved spatial predictions is, at best, inconclusive. 1. INTRODUCTION

Citations