Bayesian Semiparametric Seemingly Unrelated Regression View Full Text


Ontology type: schema:Chapter     


Chapter Info

DATE

2002

AUTHORS

Stefan Lang , Samson B. Adebayo , Ludwig Fahrmeir

ABSTRACT

Parametric seemingly unrelated regression (SUR) models are a common tool for multivariate regression analysis when error variables are reasonably correlated. 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 semiparametric SUR model based on Bayesian P-splines. Inference is fully Bayesian and uses recent Markov chain Monte Carlo techniques. More... »

PAGES

195-200

References to SciGraph publications

Book

TITLE

Compstat

ISBN

978-3-7908-1517-7
978-3-642-57489-4

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/978-3-642-57489-4_25

DOI

http://dx.doi.org/10.1007/978-3-642-57489-4_25

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1049442671


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