Noncrossing structured additive multiple-output Bayesian quantile regression models

Bayesian inference for structured additive quantile regression models

Most quantile regression problems in practice require flexible semiparametric forms of the predictor for modeling the dependence of responses on covariates. Furthermore, it is often necessary to add random effects accounting for overdispersion caused by unobserved heterogeneity or for correlation in longitudinal data. We present a unified approach for Bayesian quantile inference via Markov chai...

Bayesian semiparametric additive quantile regression

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 po...

BayesX: Analysing Bayesian structured additive regression models

SUMMARY There has been much recent interest in Bayesian inference for generalized additive and related models. The increasing popularity of Bayesian methods for these and other model classes is mainly caused by the introduction of Markov chain Monte Carlo (MCMC) simulation techniques which allow the estimation of very complex and realistic models. This paper describes the capabilities of the pu...

Additive Models for Quantile Regression

We describe some recent development of nonparametric methods for estimating conditional quantile functions using additive models with total variation roughness penalties. We focus attention primarily on selection of smoothing parameters and on the con

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