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Open Access Green as soon as Postprint is submitted to ZB.
Bayesian semiparametric additive quantile regression.
Stat. Model. 13, 223-252 (2013)
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 modelling 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. The impact of the likelihood misspecification that underlies the Bayesian formulation of quantile regression is studied in terms of simulations.
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Publication type
Article: Journal article
Document type
Scientific Article
Keywords
Quantile Regression ; Geodditive Regression ; Mcmc ; Lasso Regularization ; Dirichlet Process; Variable Selection ; Models ; Lasso ; Splines ; Regularization ; Shrinkage
ISSN (print) / ISBN
1471-082X
e-ISSN
1477-0342
Journal
Statistical Modelling
Quellenangaben
Volume: 13,
Issue: 3,
Pages: 223-252
Publisher
Sage
Non-patent literature
Publications
Reviewing status
Peer reviewed
Institute(s)
Institute of Epidemiology (EPI)