Bayesian Smoothing and Regression for Longitudinal, Spatial and Event History Data

Ludwig Fahrmeir & Thomas Kneib

Adopting a unifying, Bayesian perspective, several recent advances in smoothing and semiparametric regression are presented in this book. Simulation-based full Bayesian Markov chain Monte Carlo (MCMC) inference and 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, separate chapters then treat longitudinal data, spatial data and event history data. 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.

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