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About this product
- DescriptionSeveral 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 unkwn 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 ecomics, 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. These, as well as some of the data sets, are made publicly available on the website accompanying this book.
- Author BiographyThomas 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.
- Author(s)Ludwig Fahrmeir,Thomas Kneib
- PublisherOxford University Press
- Date of Publication28/04/2011
- Series TitleOxford Statistical Science Series
- Series Part/Volume NumberNo. 36
- Place of PublicationOxford
- Country of PublicationUnited Kingdom
- ImprintOxford University Press
- Content Note150 black and white line drawings, 10 black and white half tones
- Weight914 g
- Width161 mm
- Height240 mm
- Spine35 mm
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