Propriety of posteriors in structured additive regression models: Theory and empirical evi\-dence

DOI10.1016/j.jspi.2008.05.036zbMath1156.62029OpenAlexW1969469580MaRDI QIDQ1007463

Ludwig Fahrmeir, Thomas Kneib

Publication date: 20 March 2009

Published in: Journal of Statistical Planning and Inference (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.jspi.2008.05.036

zbMATH Keywords

Markov random fieldsMCMCpenalized splinesBayesian semiparametric regressionmixed model representationpropriety of posteriors

Mathematics Subject Classification ID

Random fields; image analysis (62M40) Nonparametric regression and quantile regression (62G08) Bayesian inference (62F15) Numerical analysis or methods applied to Markov chains (65C40)

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Uses Software

• GMRFLib
• BayesX
• SemiPar
• GAMLSS
• Fahrmeir

Cites Work

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• Flexible smoothing with \(B\)-splines and penalties. With comments and a rejoinder by the authors
• Generalized structured additive regression based on Bayesian P-splines
• Model-Based Geostatistics
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• The Effect of Improper Priors on Gibbs Sampling in Hierarchical Linear Mixed Models
• Objective Bayesian Analysis of Spatially Correlated Data
• Identifiability, Improper Priors, and Gibbs Sampling for Generalized Linear Models
• Reference Bayesian Methods for Generalized Linear Mixed Models
• Gaussian Markov Random Fields
• A Mixed Model Approach for Geoadditive Hazard Regression
• Geoadditive Survival Models
• Generalized Additive Models for Location, Scale and Shape
• Multivariate statistical modelling based on generalized linear models.
• Prior distributions for variance parameters in hierarchical models (Comment on article by Browne and Draper)