Non-linear and nonparametric modeling of seasonal environmental data
type: Item
instance of: scholarly article
title: Non-linear and nonparametric modeling of seasonal environmental data
zbMATH Open document ID: 1050.62110
author: A. McMullan
author: Adrian W. Bowman
author: E. Marian Scott
published in: Computational Statistics
publication date: 2004-11-24T00:00:00Z
review text: 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.
Mathematics Subject Classification ID: 62P12
Mathematics Subject Classification ID: 62-08
Mathematics Subject Classification ID: 62G08
Mathematics Subject Classification ID: 65C05
Mathematics Subject Classification ID: 42A10
zbMATH DE Number: 2118509
zbMATH Keywords: additive models
zbMATH Keywords: semiparametric models
zbMATH Keywords: varying coefficient models
zbMATH Keywords: approximate F test
zbMATH Keywords: pseudo-likelihood ratio test
zbMATH Keywords: seasonal effects
zbMATH Keywords: environmental data
describes a project that uses: S-PLUS
describes a project that uses: Fahrmeir
describes a project that uses: R
MaRDI profile type: Publication
full work available at URL: s001800300139
OpenAlex ID: W1554507455
cites work: Q4365239
cites work: Multivariate statistical modelling based on generalized linear models.
cites work: Q3750826
cites work: Q3998409
cites work: Q4272837
cites work: Q4356369
DOI: 10.1007/S001800300139
description: scientific article; zbMATH DE number 2118509