Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/73867 
Year of Publication: 
2012
Series/Report no.: 
Working Papers in Economics and Statistics No. 2012-06
Publisher: 
University of Innsbruck, Research Platform Empirical and Experimental Economics (eeecon), Innsbruck
Abstract: 
Quantile regression provides a convenient framework for analyzing the impact of covariates on the complete conditional distribution of a response variable instead of only the mean. While frequentist treatments of quantile regression are typically completely nonparametric, a Bayesian formulation relies on assuming the asymmetric Laplace distribution as auxiliary error distribution that yields posterior modes equivalent to frequentist estimates. In this paper, we utilize a location-scale-mixture of normals representation of the asymmetric Laplace distribution to transfer different flexible modeling concepts from Gaussian mean regression to Bayesian semiparametric quantile regression. In particular, we will consider high-dimensional geoadditive models comprising LASSO regularization priors and mixed models with potentially non-normal random effects distribution modeled via a Dirichlet process mixture. These extensions are illustrated using two large-scale applications on net rents in Munich and longitudinal measurements on obesity among children.
Subjects: 
asymmetric Laplace distribution
Bayesian quantile regression
Dirichlet process mixtures
LASSO
P-splines
Document Type: 
Working Paper

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