Bayesian Geoadditive Seemingly Unrelated Regression

Stefan Lang, Samson B. Adebayo, Ludwig Fahrmeir and Winfried J. Steiner
Additional contact information
Stefan Lang: University of Munich
Samson B. Adebayo: University of Munich
Ludwig Fahrmeir: University of Munich
Winfried J. Steiner: University of Regensburg

Computational Statistics, 2003, vol. 18, issue 2, No 6, 263-292

Abstract: Summary Parametric seemingly unrelated regression (SUR) models are a common tool for multivariate regression analysis when error variables are reasonably correlated, so that separate univariate analysis may result in inefficient estimates of covariate effects. A weakness of parametric models is that they require strong assumptions on the functional form of possibly nonlinear effects of metrical covariates. In this paper, we develop a Bayesian semiparametric SUR model, where the usual linear predictors are replaced by more flexible additive predictors allowing for simultaneous nonparametric estimation of such covariate effects and of spatial effects. The approach is based on appropriate smoothness priors which allow different forms and degrees of smoothness in a general framework. Inference is fully Bayesian and uses recent Markov chain Monte Carlo techniques.

Keywords: Bayesian semiparametric models; correlated responses; Markov random fields; MCMC; P-splines (search for similar items in EconPapers)
Date: 2003
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Citations: View citations in EconPapers (6)

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DOI: 10.1007/s001800300144

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