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Bayesian Structured Additive Distributional Regression for Multivariate Responses

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Author Info

  • Nadja Klein

    ()

  • Thomas Kneib

    ()

  • Stephan Klasen

    ()

  • Stefan Lang

    ()

Abstract

In this paper, we propose a unified Bayesian approach for multivariate structured additive distributional regression analysis where inference is applicable to a huge class of multivariate response distributions, comprising continuous, discrete and latent models, and where each parameter of these potentially complex distributions is modelled by a structured additive predictor. The latter is an additive composition of different types of covariate effects e.g. nonlinear effects of continuous variables, random effects, spatial variations, or interaction effects. Inference is realised by a generic, efficient Markov chain Monte Carlo algorithm based on iteratively weighted least squares approximations and with multivariate Gaussian priors to enforce specific properties of functional effects. Examples will be given by illustrations on analysing the joint model of risk factors for chronic and acute childhood malnutrition in India and on ecological regression for German election results.

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File URL: http://eeecon.uibk.ac.at/wopec2/repec/inn/wpaper/2013-35.pdf
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Bibliographic Info

Paper provided by Faculty of Economics and Statistics, University of Innsbruck in its series Working Papers with number 2013-35.

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Length: 52 pages
Date of creation: Nov 2013
Date of revision:
Handle: RePEc:inn:wpaper:2013-35

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Related research

Keywords: correlated responses; iteratively weighted least squares proposal; Markov chain Monte Carlo simulation; penalised splines; semiparametric regression; Dirichlet regression; seemingly unrelated regression;

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