Arbeitspapier

Generalized structured additive regression based on Bayesian P-splines

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Generalized additive models (GAM) for modelling nonlinear effects of continuous covariates are now well established tools for the applied statistician. In this paper we develop Bayesian GAM?s and extensions to generalized structured additive regression based on one or two dimensional P-splines as the main building block. The approach extends previous work by Lang and Brezger (2003) for Gaussian responses. Inference relies on Markov chain Monte Carlo (MCMC) simulation techniques, and is either based on iteratively weighted least squares (IWLS) proposals or on latent utility representations of (multi)categorical regression models. Our approach covers the most common univariate response distributions, e.g. the Binomial, Poisson or Gamma distribution, as well as multicategorical responses. As we will demonstrate through two applications on the forest health status of trees and a space-time analysis of health insurance data, the approach allows realistic modelling of complex problems. We consider the enormous flexibility and extendability of our approach as a main advantage of Bayesian inference based on MCMC techniques compared to more traditional approaches. Software for the methodology presented in the paper is provided within the public domain package BayesX.

Sprache
Englisch

Erschienen in
Series: Discussion Paper ; No. 321

Thema
geoadditive models
IWLS proposals
multicategorical response
structured additive predictors
surface smoothing
Varianzanalyse

Ereignis
Geistige Schöpfung
(wer)
Brezger, Andreas
Lang, Stefan
Ereignis
Veröffentlichung
(wer)
Ludwig-Maximilians-Universität München, Sonderforschungsbereich 386 - Statistische Analyse diskreter Strukturen
(wo)
München
(wann)
2003

DOI
doi:10.5282/ubm/epub.1702
Handle
URN
urn:nbn:de:bvb:19-epub-1702-5
Letzte Aktualisierung
20.09.2024, 08:21 MESZ

Datenpartner

Dieses Objekt wird bereitgestellt von:
ZBW - Deutsche Zentralbibliothek für Wirtschaftswissenschaften - Leibniz-Informationszentrum Wirtschaft. Bei Fragen zum Objekt wenden Sie sich bitte an den Datenpartner.
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Objekttyp

  • Arbeitspapier

Beteiligte

  • Brezger, Andreas
  • Lang, Stefan
  • Ludwig-Maximilians-Universität München, Sonderforschungsbereich 386 - Statistische Analyse diskreter Strukturen

Entstanden

  • 2003

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