Jump to ContentJump to Main Navigation
Bayesian Smoothing and Regression for Longitudinal, Spatial and Event History Data$
Users without a subscription are not able to see the full content.

Ludwig Fahrmeir and Thomas Kneib

Print publication date: 2011

Print ISBN-13: 9780199533022

Published to Oxford Scholarship Online: September 2011

DOI: 10.1093/acprof:oso/9780199533022.001.0001

Show Summary Details
Page of

PRINTED FROM OXFORD SCHOLARSHIP ONLINE (www.oxfordscholarship.com). (c) Copyright Oxford University Press, 2019. All Rights Reserved. An individual user may print out a PDF of a single chapter of a monograph in OSO for personal use. date: 15 October 2019

Spatial Smoothing, Interactions and Geoadditive Regression

Spatial Smoothing, Interactions and Geoadditive Regression

Chapter:
(p.307) 5 Spatial Smoothing, Interactions and Geoadditive Regression
Source:
Bayesian Smoothing and Regression for Longitudinal, Spatial and Event History Data
Author(s):

Ludwig Fahrmeir

Thomas Kneib

Publisher:
Oxford University Press
DOI:10.1093/acprof:oso/9780199533022.003.0005

This chapter provides an introduction to Bayesian spatial smoothing as a subject of interest in its own right, it points out the close relation between modelling interactions and spatial effects, and it extends smoothing and regression to geoadditive regression models. Section 5.1 introduces the different types of spatial data in more detail and provides information on the corresponding modelling techniques. Section 5.2 describes Markov random fields as basic stochastic process models for discrete spatial data. Section 5.3 highlights relations between continuous spatial smoothing approaches and the modelling of interactions. Section 5.4 introduces Gaussian random fields as stochastic process models for continuous spatial data, including their use in classical geostatistics. Section 5.5 incorporates ideas from the previous sections in the general framework of geoadditive regression.

Keywords:   Bayesian spatial smoothing, geoadditive regression models, Markov random fields, Gaussian random fields, stochastic process models

Oxford Scholarship Online requires a subscription or purchase to access the full text of books within the service. Public users can however freely search the site and view the abstracts and keywords for each book and chapter.

Please, subscribe or login to access full text content.

If you think you should have access to this title, please contact your librarian.

To troubleshoot, please check our FAQs , and if you can't find the answer there, please contact us .