Non-linear and nonparametric modeling of seasonal environmental data
The aim of this paper is to show how nonparametric methods can increase potential and flexibility of nonlinear models by allowing appropriate terms to vary smoothly over time. For this purpose the general structure of additive, semiparametric and varying coefficient models within a nonlinear setting are discussed. Such models turn out to be rather useful in environmental applications, for example to incorporate seasonal effects. Analysis of data on water quality from the River Clyde are used as an example.
scientific article; zbMATH DE number 2118509