Bayesian Smoothing and Regression for Longitudinal, Spatial and Event History Data by Ludwig Fahrmeir

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

byLudwig Fahrmeir, Thomas Kneib

Hardcover | May 14, 2011

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Several recent advances in smoothing and semiparametric regression are presented in this book from a unifying, Bayesian perspective. Simulation-based full Bayesian Markov chain Monte Carlo (MCMC) inference, as well as empirical Bayes procedures closely related to penalized likelihoodestimation and mixed models, are considered here. Throughout, the focus is on semiparametric regression and smoothing based on basis expansions of unknown functions and effects in combination with smoothness priors for the basis coefficients.Beginning with a review of basic methods for smoothing and mixed models, longitudinal data, spatial data and event history data are treated in separate chapters. Worked examples from various fields such as forestry, development economics, medicine and marketing are used to illustrate the statisticalmethods covered in this book. Most of these examples have been analysed using implementations in the Bayesian software, BayesX, and some with R Codes. These, as well as some of the data sets, are made publicly available on the website accompanying this book.

About The Author

Ludwig Fahrmeir is Professor Emeritus in the Department of Statistics at Ludwig-Maximilians-University Munich. He has been Professor of Statistics at the University of Regensburg, Chairman of the Collaborative Research Centre "Statistical Analysis of Discrete Structures with Applications in Econometrics and Biometrics" and was coordin...
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Details & Specs

Title:Bayesian Smoothing and Regression for Longitudinal, Spatial and Event History DataFormat:HardcoverDimensions:536 pagesPublished:May 14, 2011Publisher:Oxford University PressLanguage:English

The following ISBNs are associated with this title:

ISBN - 10:0199533024

ISBN - 13:9780199533022

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Table of Contents

1. Introduction: Scope of the Book and Applications2. Basic Concepts for Smoothing and Semiparametric Regression3. Generalised Linear Mixed Models4. Semiparametric Mixed Models for Longitudinal Data5. Spatial Smothing, Interactions and Geoadditive Regression6. Event History Data