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Modelling of South African Hypertension: Comparative Analysis of the Classical and Bayesian Quantile Regression Approaches [PDF]
Inquiry: The Journal of Health Care Organization, Provision, and Financing, 2022 Hypertension has become a major public health challenge and a crucial area of research due to its high prevalence across the world including the sub-Saharan Africa.
Anesu Gelfand Kuhudzai PhD candidate +3 more
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Power Prior Elicitation in Bayesian Quantile Regression [PDF]
Journal of Probability and Statistics, 2011 We address a quantile dependent prior for Bayesian quantile regression. We extend the idea of the power prior distribution in Bayesian quantile regression by employing the likelihood function that is based on a location-scale mixture representation of ...
Rahim Alhamzawi, Keming Yu
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Modeling Spatial Data with Heteroscedasticity Using PLVCSAR Model: A Bayesian Quantile Regression Approach [PDF]
EntropySpatial data not only enables smart cities to visualize, analyze, and interpret data related to location and space, but also helps departments make more informed decisions.
Rongshang Chen, Zhiyong Chen
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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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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
Genya Kobayashi
exaly +3 more sources
Bayesian regularized quantile regression
Bayesian Analysis, 2010 zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Ruibin Xi
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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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