Results 11 to 20 of about 44,356 (305)
Penalized flexible Bayesian quantile regression [PDF]
Applied Mathematics, 2012 Copyright © 2012 SciResThis article has been made available through the Brunel Open Access Publishing Fund.The selection of predictors plays a crucial role in building a multiple regression model. Indeed, the choice of a suitable subset of predictors can
Alhamzawi, R, Alkenani, A, Yu, K
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Bayesian adaptive lasso quantile regression [PDF]
Statistical Modelling, 2012 Recently, variable selection by penalized likelihood has attracted much research interest. In this paper, we propose adaptive Lasso quantile regression (BALQR) from a Bayesian perspective.
Al-Hamzawi, Rahim +2 more
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Bayesian Lasso-mixed quantile regression [PDF]
Journal of Statistical Computation and Simulation, 2012 In this paper, we discuss the regularization in linear-mixed quantile regression. A hierarchical Bayesian model is used to shrink the fixed and random effects towards the common population values by introducing an l1 penalty in the mixed quantile ...
Alhamzawi R. +7 more
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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 ...
Sung Jae Jun, Tony Lancaster
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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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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.
europepmc +3 more sources
Bayesian Quantile Regression for Single-Index Models [PDF]
Statistics and Computing, 2011 Using an asymmetric Laplace distribution, which provides a mechanism for Bayesian inference of quantile regression models, we develop a fully Bayesian approach to fitting single-index models in conditional quantile regression.
Gramacy, Robert B. +2 more
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Bayesian semiparametric additive quantile regression [PDF]
, 2012 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.
Kneib, Thomas +3 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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