Results 11 to 20 of about 9,896 (259)
Regularized Bayesian quantile regression [PDF]
Communications in Statistics - Simulation and Computation, 2017 A number of nonstationary models have been developed to estimate extreme events as function of covariates. A quantile regression (QR) model is a statistical approach intended to estimate and conduct inference about the conditional quantile functions.
Salaheddine El Adlouni +2 more
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Journal of Water and Climate Change, 2023 This research utilized Bayesian and quantile regression techniques to analyze trends in discharge levels across various seasons for three stations in the Gorganroud basin of northern Iran. The study spanned a period of 50 years (1966–2016).
Khalil Ghorbani +3 more
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Bayesian Spatial Quantile Regression [PDF]
Journal of the American Statistical Association, 2011 Tropospheric ozone is one of the six criteria pollutants regulated by the United States Environmental Protection Agency under the Clean Air Act and has been linked with several adverse health effects, including mortality. Due to the strong dependence on weather conditions, ozone may be sensitive to climate change and there is great interest in studying
Reich, Brian J. +2 more
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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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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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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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