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Bayesian Smoothed Quantile Regression [PDF]
68 pages, 3 figures, 4 tables, 2 algorithms. JEL Classification: Primary: C21; secondary: C11, C14. The source code and data to replicate all results are publicly available at https://github.com/BeauquinLau ...
Bingqi Liu, Kangqiang Li, Tianxiao Pang
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Bayesian Spatial Quantile Regression [PDF]
Journal of the American Statistical Association, 2011 Tropospheric ozone is one of the six criteria pollutants regulated by the United States Environmental Protection Agency under the Clean Air Act and has been linked with several adverse health effects, including mortality. Due to the strong dependence on weather conditions, ozone may be sensitive to climate change and there is great interest in studying
Reich, Brian J. +2 more
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Horseshoe prior Bayesian quantile regression
Journal of the Royal Statistical Society Series C: Applied Statistics, 2023 Abstract This paper extends the horseshoe prior to Bayesian quantile regression and provides a fast sampling algorithm for computation in high dimensions. Compared to alternative shrinkage priors, our method yields better performance in coefficient bias and forecast error, especially in sparse designs and in estimating extreme quantiles.
Kohns, D, Szendrei, Tibor
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Bayesian semiparametric additive quantile regression [PDF]
Statistical Modelling, 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
Yue, Yu Ryan +4 more
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qgam: Bayesian Nonparametric Quantile Regression Modeling in R
Journal of Statistical Software, 2021 Generalized additive models (GAMs) are flexible non-linear regression models, which can be fitted efficiently using the approximate Bayesian methods provided by the mgcv R package.
Matteo Fasiolo +4 more
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A Bayesian Variable Selection Method for Spatial Autoregressive Quantile Models
Mathematics, 2023 In this paper, a Bayesian variable selection method for spatial autoregressive (SAR) quantile models is proposed on the basis of spike and slab prior for regression parameters.
Yuanying Zhao, Dengke Xu
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Bayesian quantile semiparametric mixed-effects double regression models
Statistical Theory and Related Fields, 2021 Semiparametric mixed-effects double regression models have been used for analysis of longitudinal data in a variety of applications, as they allow researchers to jointly model the mean and variance of the mixed-effects as a function of predictors ...
Duo Zhang +3 more
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Quantity and Quality in Scientific Productivity: The Tilted Funnel Goes Bayesian
Journal of Intelligence, 2022 The equal odds baseline model of creative scientific productivity proposes that the number of high-quality works depends linearly on the number of total works.
Boris Forthmann, Denis Dumas
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Bayesian composite quantile regression for the single-index model.
PLoS ONE, 2023 By using a Gaussian process prior and a location-scale mixture representation of the asymmetric Laplace distribution, we develop a Bayesian analysis for the composite quantile single-index regression model.
Xiaohui Yuan, Xuefei Xiang, Xinran Zhang
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Bayesian quantile additive regression trees [PDF]
, 2016 23 pages, 3 ...
Bereket P. Kindo +3 more
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