Bayesian Smoothing and Regression for Longitudinal, Spatial and Event History Data / Edition 1 available in Hardcover
- Pub. Date:
- Oxford University Press, USA
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 analyzed using implementations in the Bayesian software, BayesX, and some with R Codes.
About the Author
Ludwig Fahrmeir is Professor Emeritus, Department of Statistics, 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 coordinator of the project "Analysis and Modelling of Complex Systems in Biology and Medicine" at the University of Munich. He is an Elected Fellow of the International Statistical Institute.
Thomas Kneib received a PhD in Statistics in 2006 from the University of Munich. He has been visiting Professor for Applied Statistics at the University of Ulm and Professor for Statistics at the University of Gottingen. Currently, he is Professor for Applied Statistics at the University of Oldenburg.
Table of Contents
1. Introduction: Scope of the Book and Applications
2. Basic Concepts for Smoothing and Semiparametric Regression
3. Generalised Linear Mixed Models
4. Semiparametric Mixed Models for Longitudinal Data
5. Spatial Smothing, Interactions and Geoadditive Regression
6. Event History Data