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Bayesian quantile regression [PDF]
Working Paper Series, 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.
Adlouni, Salaheddine El +2 more
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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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Generative AI for Bayesian Computation [PDF]
EntropyGenerative Bayesian Computation (GBC) provides a simulation-based approach to Bayesian inference. A Quantile Neural Network (QNN) is trained to map samples from a base distribution to the posterior distribution.
Nick Polson, Vadim Sokolov
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Deep Evidential Learning for Bayesian Quantile Regression [PDF]
arXiv.org, 2023 It is desirable to have accurate uncertainty estimation from a single deterministic forward-pass model, as traditional methods for uncertainty quantification are computationally expensive.
F. B. Hüttel +2 more
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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 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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Journal of Water and Climate Change, 2023 This research utilized Bayesian and quantile regression techniques to analyze trends in discharge levels across various seasons for three stations in the Gorganroud basin of northern Iran. The study spanned a period of 50 years (1966–2016).
Khalil Ghorbani +3 more
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High-Dimensional Variable Selection for Quantile Regression Based on Variational Bayesian Method
Mathematics, 2023 The quantile regression model is widely used in variable relationship research of moderate sized data, due to its strong robustness and more comprehensive description of response variable characteristics.
Dengluan Dai, Anmin Tang, Jinli Ye
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