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Bayesian Smoothing and Regression for Longitudinal, Spatial and Event History Data$
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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

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Introduction: Scope of the Book and Applications

Introduction: Scope of the Book and Applications

Chapter:
(p.1) 1 Introduction: Scope of the Book and Applications
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/9780199533022.003.0001

This introductory chapter begins with a discussion of semiparametric regression covering generalized linear models, generalized additive models, semiparametric mixed models, and spatial regression models. It then presents a number of examples that will serve as illustrations throughout the book. Together with the description of some of the key features of different data sets, the chapter also relates the previously discussed predictors and model classes to specific applications.

Keywords:   semiparametric regression, covering generalized linear models, generalized additive models, semiparametric mixed models, spatial regression models

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