Bayesian Structured Additive Distributional Regression for Multivariate Responses
AbstractIn 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.
Download InfoIf you experience problems downloading a file, check if you have the proper application to view it first. In case of further problems read the IDEAS help page. Note that these files are not on the IDEAS site. Please be patient as the files may be large.
Bibliographic InfoPaper provided by Faculty of Economics and Statistics, University of Innsbruck in its series Working Papers with number 2013-35.
Length: 52 pages
Date of creation: Nov 2013
Date of revision:
Contact details of provider:
Postal: Universitätsstraße 15, A - 6020 Innsbruck
Web page: http://www.uibk.ac.at/fakultaeten/volkswirtschaft_und_statistik/index.html.en
More information through EDIRC
correlated responses; iteratively weighted least squares proposal; Markov chain Monte Carlo simulation; penalised splines; semiparametric regression; Dirichlet regression; seemingly unrelated regression;
This paper has been announced in the following NEP Reports:
- NEP-ALL-2013-11-16 (All new papers)
- NEP-DCM-2013-11-16 (Discrete Choice Models)
- NEP-ECM-2013-11-16 (Econometrics)
- NEP-ORE-2013-11-16 (Operations Research)
You can help add them by filling out this form.
reading list or among the top items on IDEAS.Access and download statisticsgeneral information about how to correct material in RePEc.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Janette Walde).
If references are entirely missing, you can add them using this form.