Results 11 to 20 of about 2,329,699 (248)
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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Prior elicitation and variable selection for bayesian quantile regression [PDF]
, 2013 This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.Bayesian subset selection suffers from three important difficulties: assigning priors over model space, assigning priors to all components of the regression
Al-Hamzawi, Rahim Jabbar Thaher
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Sparse Quantile Regression [PDF]
Journal of Econometrics, 2023 We consider both $\ell _{0}$-penalized and $\ell _{0}$-constrained quantile regression estimators. For the $\ell _{0}$-penalized estimator, we derive an exponential inequality on the tail probability of excess quantile prediction risk and apply it to obtain non-asymptotic upper bounds on the mean-square parameter and regression function estimation ...
Lee, Sokbae (Simon), Chen, Le-Yu
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Function-on-function linear quantile regression
Mathematical Modelling and Analysis, 2022 In this study, we propose a function-on-function linear quantile regression model that allows for more than one functional predictor to establish a more flexible and robust approach. The proposed model is first transformed into a finitedimensional space
Ufuk Beyaztas, Han Lin Shang
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Quantile Regression with Generated Regressors
Econometrics, 2021 This paper studies estimation and inference for linear quantile regression models with generated regressors. We suggest a practical two-step estimation procedure, where the generated regressors are computed in the first step. The asymptotic properties of
Liqiong Chen +2 more
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Desimal, 2023 The censored quantile regression method is a parameter estimation method that can be used to overcome censored data and BLUE (Best Linear Unbiased Estimator) assumptions that are not met.
Sarmada Sarmada +2 more
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A partially collapsed Gibbs sampler for Bayesian quantile regression [PDF]
, 2009 We introduce a set of new Gibbs sampler for Bayesian analysis of quantile re-gression model. The new algorithm, which partially collapsing an ordinary Gibbs sampler, is called Partially Collapsed Gibbs (PCG) sampler.
Yu, K, Reed, C
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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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A Bayesian Binary reciprocal LASSO quantile regression (with practical application)
Journal of Kufa for Mathematics and Computer, 2023 Quantile regression is one of the methods that has taken a wide space in application in the previous two decades because of the attractive features of these methods to researchers, as it is not affected by outliers values, meaning that it is considered ...
Mohammed Kahnger, Ahmad Naeem Flaih
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Pyramid Quantile Regression [PDF]
Journal of Computational and Graphical Statistics, 2019 Quantile regression models provide a wide picture of the conditional distributions of the response variable by capturing the effect of the covariates at different quantile levels. In most applications, the parametric form of those conditional distributions is unknown and varies across the covariate space, so fitting the given quantile levels ...
T. Rodrigues +2 more
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