Bayesian smoothing and regression for longitudinal, spatial and event history data

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Title:

Bayesian smoothing and regression for longitudinal, spatial and event history data

Author:

Fahrmeir, Ludwig, 1945-.

ISBN:

9780199533022

Publication Information:

Oxford : Oxford University Press, 2011.

Physical Description:

xviii, 521 s. : ill. ; 24 cm.

Series:

Oxford statistical science series ; 36

General Note:

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.

Added Author:

Fahrmeir, Ludwig, 1945-.

Kneib, Thomas.

Copies:

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Library	Material Type	Item Barcode	Shelf Number	Copy	Status
Searching... IIEMSA	General Books	33168025564184	519.536 F158B 2011	1	Searching... Unknown

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Summary

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.