|
|
|
|
Bayesian Smoothing and Regression for Longitudinal, Spatial and Event History Data
Ludwig Fahrmeir and Thomas Kneib
544 pages
|
150 black and white line drawings, 10 black and white half tones
|
234x156mm
978-0-19-953302-2
|
Hardback
|
28 April 2011
|
|
|
|
|
- Unifies several seemingly disparate model formulations
- Considers both full and empirical Bayes inference
- Up-to-date treatment of longitudinal, spatial and event history data in a regression context
- Applications from diverse fields such as forestry, development economics, medicine, and marketing
- Offers a balance between theory and its applications
- Worked examples of all methods covered in book
- Accompanying website containing codes and some of the data sets used in the book
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. These, as well as some of the data sets, are made publicly available on the website accompanying this book.Readership: Suitable for graduates, PhD students and their lecturers as a basis, or as additional material, for courses in statistics, biostatistics and econometrics. Also suitable for researchers in applied statistics, quantitative economics, the social
sciences and the life sciences.
|
|
|
Ludwig Fahrmeir, Department of Statistics, Ludwig Maxmilians University, Munich, Germany, and Thomas Kneib, Department of Statistics, Ludwig Maxmilians University, Munich, Germany Link to AUTHOR'S page about this book
|
|
|
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
|
|
|
|
The specification in this catalogue, including without limitation price, format, extent, number of illustrations, and month of publication, was as accurate as possible at the time the catalogue was compiled. Occasionally, due to the nature of some contractual restrictions, we are unable to ship a specific product to a particular territory. Jacket images are provisional and liable to change before publication.
|
|
