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Bayesian quantile regression [PDF]
, 2006 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 ...
Tony Lancaster, Sung Jae Jun
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A Bayesian Approach to Envelope Quantile Regression
Statistica Sinica, 2022 Summary: The enveloping approach employs sufficient dimension-reduction techniques to gain estimation efficiency, and has been used in several multivariate analysis contexts. However, its Bayesian development has been sparse, and the only Bayesian envelope construction is in the context of a linear regression.
Lee*, Minji +2 more
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Bayesian quantile semiparametric mixed-effects double regression models
Statistical Theory and Related Fields, 2021 Semiparametric mixed-effects double regression models have been used for analysis of longitudinal data in a variety of applications, as they allow researchers to jointly model the mean and variance of the mixed-effects as a function of predictors ...
Duo Zhang +3 more
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A Bayesian Variable Selection Method for Spatial Autoregressive Quantile Models
Mathematics, 2023 In this paper, a Bayesian variable selection method for spatial autoregressive (SAR) quantile models is proposed on the basis of spike and slab prior for regression parameters.
Yuanying Zhao, Dengke Xu
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Bayesian semiparametric additive quantile regression [PDF]
Statistical Modelling, 2013 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. While frequentist treatments of quantile regression are typically completely nonparametric, a Bayesian formulation relies on assuming the asymmetric Laplace distribution as ...
Yue, Yu Ryan +4 more
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Bayesian composite quantile regression for the single-index model.
PLoS ONE, 2023 By using a Gaussian process prior and a location-scale mixture representation of the asymmetric Laplace distribution, we develop a Bayesian analysis for the composite quantile single-index regression model.
Xiaohui Yuan, Xuefei Xiang, Xinran Zhang
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Quantity and Quality in Scientific Productivity: The Tilted Funnel Goes Bayesian
Journal of Intelligence, 2022 The equal odds baseline model of creative scientific productivity proposes that the number of high-quality works depends linearly on the number of total works.
Boris Forthmann, Denis Dumas
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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. The method extends the Bayesian Lasso quantile regression by allowing different penalization parameters for different regression coefficients.
R. ALHAMZAWI, K. YU, D. F. BENOIT
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Bayesian parameter estimation and variable selection for quantile regression [PDF]
, 2011 This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.The principal goal of this work is to provide efficient algorithms for implementing the Bayesian approach to quantile regression.
Reed, Craig
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M-quantile regression analysis of temporal gene expression data [PDF]
, 2009 In this paper, we explore the use of M-regression and M-quantile coefficients to detect statistical differences between temporal curves that belong to different experimental conditions.
Vinciotti, V, Yu, K
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