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

Ludwig Fahrmeir and Thomas Kneib

Abstract

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 likelihood estimation 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, ... More

Keywords: smoothing, semiparametric regression, Bayesian perspective, Markov chain Monte Carlo, MCMC, forestry, developmental economics, medicine, marketing, BayesX

Bibliographic Information

Print publication date: 2011 Print ISBN-13: 9780199533022
Published to Oxford Scholarship Online: September 2011 DOI:10.1093/acprof:oso/9780199533022.001.0001

Authors

Affiliations are at time of print publication.

Ludwig Fahrmeir, author
Department of Statistics, Ludwig Maxmilians University, Munich, Germany
Author Webpage

Thomas Kneib, author
Department of Statistics, Ludwig Maxmilians University, Munich, Germany
Author Webpage

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