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
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, 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 statistical methods covered in this book. Most of these examples have been analysed using implementations in the Bayesian software, BayesX, and some with R Codes.
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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