Bayesian semiparametric additive quantile regression

Arbeitspapier

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.

Object Details

• Language: Englisch

References and Relationships

• Bibliographic citation: Series: Working Papers in Economics and Statistics ; No. 2012-06

Classification and Topics

• Classification: Wirtschaft
• Subject: asymmetric Laplace distribution ; Bayesian quantile regression ; Dirichlet process mixtures ; LASSO ; P-splines

Contributors, Places and Time

• Event: Geistige Schöpfung
• (who): Waldmann, Elisabeth ; Kneib, Thomas ; Yu, Yu Ryan ; Lang, Stefan
• Event: Veröffentlichung
• (who): University of Innsbruck, Research Platform Empirical and Experimental Economics (eeecon)
• (where): Innsbruck
• (when): 2012

Further information

• Handle: http://hdl.handle.net/10419/73867
• Last update: 10.03.2025, 11:43 AM CET