Results 271 to 280 of about 44,356 (305)

Bayesian Quantile Regression

2017
This chapter provides a review of Bayesian quantile regression methods based on different types of working likelihoods. It discusses some developments in Bayesian quantile regression approaches based on various working likelihoods, including parametric likelihood based on the asymmetric Laplace distribution, empirical likelihood, and some ...
Huixia Judy Wang, Yunwen Yang
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Bayesian quantile regression methods

Journal of Applied Econometrics, 2010
AbstractThis paper is a study of the application of Bayesian exponentially tilted empirical likelihood to inference about quantile regressions. In the case of simple quantiles we show the exact form for the likelihood implied by this method and compare it with the Bayesian bootstrap and with Jeffreys' method.
Tony Lancaster, Sung Jae Jun
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Bayesian bridge quantile regression

Communications in Statistics - Simulation and Computation, 2018
Regularization methods for simultaneous variable selection and coefficient estimation have been shown to be effective in quantile regression in improving the prediction accuracy.
Rahim Alhamzawi, Zakariya Yahya Algamal
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Bayesian composite quantile regression

Journal of Statistical Computation and Simulation, 2015
One advantage of quantile regression, relative to the ordinary least-square (OLS) regression, is that the quantile regression estimates are more robust against outliers and non-normal errors in the response measurements. However, the relative efficiency of the quantile regression estimator with respect to the OLS estimator can be arbitrarily small.
Hanwen Huang, Zhongxue Chen
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Variational Bayesian Tensor Quantile Regression

Acta Mathematica Sinica, English Series
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Jin, Yunzhi, Zhang, Yanqing
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Bayesian Semiparametric Modelling in Quantile Regression

Scandinavian Journal of Statistics, 2009
Abstract.  We propose a Bayesian semiparametric methodology for quantile regression modelling. In particular, working with parametric quantile regression functions, we develop Dirichlet process mixture models for the error distribution in an additive quantile regression formulation.
Kottas, Athanasios, Krnjajić, Milovan
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Parameter estimation of Bayesian quantile regression

AIP Conference Proceedings, 2021
Quantile regression is a regression method that modelling a relationship between quantile of variable response and one or more variable predictors. Quantile regression has advantages that linear regression does not have; it is robust against outliers and can model heteroscedasticity data. The parameters of quantile regression can be estimated using the
D. Dichandra, I. Fithriani, S. Nurrohmah
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Bayesian composite Tobit quantile regression

Journal of Applied Statistics, 2017
Composite quantile regression models have been shown to be effective techniques in improving the prediction accuracy [H. Zou and M.
Fadel Hamid Hadi Alhusseini, Vasile Georgescu   +1 more
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Bayesian group bridge composite quantile regression

Journal of Namibian Studies : History Politics Culture, 2023
Bayesian regularized composite quantile regression (CQR) method with group bridge penalty is adopted to conduct covariate selection and estimation in CQR. MCMC algorithm was improved for posterior inference employing a scale mixture of normal of the asymmetric Laplace distribution (ALD).
null Mayyadah Aljasimee, null Rahim Alhamzawi   +1 more
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Bayesian Nonparametric Quantile Process Regression

2021
Codes to reproduce QUINN, a novel Bayesian nonparametric quantile process regression.
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