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Horseshoe prior Bayesian quantile regression
Journal of the Royal Statistical Society Series C: Applied Statistics, 2023 Abstract This paper extends the horseshoe prior to Bayesian quantile regression and provides a fast sampling algorithm for computation in high dimensions. Compared to alternative shrinkage priors, our method yields better performance in coefficient bias and forecast error, especially in sparse designs and in estimating extreme quantiles.
Kohns, D, Szendrei, Tibor
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qgam: Bayesian Nonparametric Quantile Regression Modeling in R
Journal of Statistical Software, 2021 Generalized additive models (GAMs) are flexible non-linear regression models, which can be fitted efficiently using the approximate Bayesian methods provided by the mgcv R package.
Matteo Fasiolo +4 more
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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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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 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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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 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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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 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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Quantile Regression Neural Networks: A Bayesian Approach [PDF]
Journal of Statistical Theory and Practice, 2021 This article introduces a Bayesian neural network estimation method for quantile regression assuming an asymmetric Laplace distribution (ALD) for the response variable. It is shown that the posterior distribution for feedforward neural network quantile regression is asymptotically consistent under a misspecified ALD model. This consistency proof embeds
S. R. Jantre, S. Bhattacharya, T. Maiti
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