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bayesQR: A Bayesian Approach to Quantile Regression [PDF]
Journal of Statistical Software, 2017 After its introduction by Koenker and Basset (1978), quantile regression has become an important and popular tool to investigate the conditional response distribution in regression. The R package bayesQR contains a number of routines to estimate quantile
Dries F. Benoit, Dirk Van den Poel
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Gibbs sampling methods for Bayesian quantile regression [PDF]
Journal of Statistical Computation and Simulation, 2011 This paper considers quantile regression models using an asymmetric Laplace distribution from a Bayesian point of view. We develop a simple and efficient Gibbs sampling algorithm for fitting the quantile regression model based on a location-scale mixture representation of the asymmetric Laplace distribution. It is shown that the resulting Gibbs sampler
Hideo Kozumi, Genya Kobayashi
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Bayesian regularized quantile regression
Bayesian Analysis, 2010 zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Nan Lin
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How Does Industrial Waste Gas Emission Affect Health Care Expenditure in Different Regions of China: An Application of Bayesian Quantile Regression. [PDF]
Int J Environ Res Public Health, 2019 Xu X, Xu Z, Chen L, Li C.
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Bayesian Smoothed Quantile Regression [PDF]
The standard asymmetric Laplace framework for Bayesian quantile regression (BQR) suffers from a fundamental decision-theoretic misalignment, yielding biased finite-sample estimates, and precludes gradient-based computation due to non-smoothness. We propose Bayesian smoothed quantile regression (BSQR), a principled framework built on a kernelsmoothed ...
Bingqi Liu, Kangqiang Li, Tianxiao Pang
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Modified Quantile Regression for Modeling the Low Birth Weight
Frontiers in Applied Mathematics and Statistics, 2022 This study aims to identify the best model of low birth weight by applying and comparing several methods based on the quantile regression method's modification.
Ferra Yanuar +2 more
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Cauchy: Jurnal Matematika Murni dan Aplikasi, 2021 This study aims to construct the model for the length of hospital stay for patients with COVID-19 using quantile regression and Bayesian quantile approaches.
Ferra Yanuar +4 more
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Bayesian quantile regression [PDF]
SSRN Electronic Journal, 2005 Recent work by Schennach(2005) has opened the way to a Bayesian treatment of quantile regression. Her method, called Bayesian exponentially tilted empirical likelihood (BETEL), provides a likelihood for data y subject only to a set of m moment conditions of the form Eg(y, θ) = 0 where θ is a k dimensional parameter of interest and k may be smaller ...
Tony Lancaster, Sung Jae Jun
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Regularized Bayesian quantile regression [PDF]
Communications in Statistics - Simulation and Computation, 2017 A number of nonstationary models have been developed to estimate extreme events as function of covariates. A quantile regression (QR) model is a statistical approach intended to estimate and conduct inference about the conditional quantile functions.
Salaheddine El Adlouni +2 more
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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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