Bayesian quantile regression

By T. Lancaster, Sung Jae Jun. 2006

620 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/907ddcee37f050f0dd63ba157cefe92a148ab185

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

In Statistics and Probability

Citations in the corpus

Posterior Consistency of Bayesian Quantile Regression Based on the Misspecified Asymmetric Laplace Density (2013, positive): “Yu and Moyeed (2001) proposed the idea of Bayesian quantile regression by assuming ALD for the response.”
Simultaneous Linear Quantile Regression: A Semiparametric Bayesian Approach (2012, methodology): “This approach also influenced early attempts at a Bayesian analysis of (1) with a conditional sampling density for the response constructed as Y = β0(τ) + x′β(τ) + σε with ε having the asymmetric Laplace density fε(2) = const × exp[−2(τ − I(2 < 0))] (Yu and Moyeed 2001; Tsionas 2003).”
Approximate Bayesian inference for quantiles (2005, methodology): “Yu and Moyeed [12] proposed the use of the asymmetric Laplace distribution for modeling of error residuals in a linear regression model in order to obtain estimates that minimize the appropriate quantile regression loss function.”
A generalized class of skew distributions and associated robust quantile regression models (2014, background): “…another popular skew distribution, partly because of its application in quantile regression problems, see Koenker & Machado (1999) and especially for Bayesian quantile regression: see, among others, Yu & Moyeed (2001), Tsionas (2003), Chen, Gerlach, & Wei (2009), and Gerlach, Chen, & Chan (2011).”
A Bayesian Nonparametric Approach to Inference for Quantile Regression (2010, methodology): “Regarding quantile regression, Yu and Moyeed (2001) and Tsionas (2003) discussed parametric inference based on linear regression functions and the asymmetric Laplace distribution for the errors, whereas Kottas and Krnjajić (2009) developed Bayesian semiparametric models using DP mixtures for the…”
Posterior Inference in Bayesian Quantile Regression with Asymmetric Laplace Likelihood (2016, methodology): “ˇ/ exp ´ Pn iD1 .yi x>i ˇ/ μ : (2.3) Any reasonable choice of the prior, including the flat prior, leads to a proper posterior under some mild conditions (Yu & Moyeed, 2001; Tsionas, 2003; Choi & Hobert, 2013).”
Bayesian Semiparametric Modelling in Quantile Regression (2009, methodology): “As in Yu & Moyeed (2001), we use a quadratic quantile regression model 0 + 1x + 2x2, where x denotes age in years.”
A quantile parametric mixed regression model for bounded response variables (2017, background): “From a Bayesian perspective, [41] proposed to assume an asymmetric Laplace distribution (ALD) in a parametric quantile regression model for an unbounded response variable.”
Bayesian Time-Varying Quantile Forecasting for Value-at-Risk in Financial Markets (2011, methodology): “The MCMC scheme is adaptive, expanding the MCMC methods for general quantile regression models in Yu and Moyeed (2001), Tsionas (2003), and Geraci and Bottai (2007).”
A Three-Parameter Asymmetric Laplace Distribution and Its Extension (2005, background): “Recently, two papers by Koenker and Machado ( 1999 ) and Yu and Moyeed ( 2001 ) have appeared in statistical journals applying a new skew distribution for quantile regression.”
Instrumental Variable Quantile Regression (2004, methodology): “Within the conventional GMM framework, one option is to take the quasi-Bayesian approach of Chernozhukov and Hong (2003); see also Wang and Yang (2016) in this handbook for a review of subsequent work on related methods.”
Bayesian quantile regression for longitudinal data models (2012, methodology): “This skewed distribution appeared in the paper by Koenker and Machado [3] and Yu and Moyeed [4], among others.”
Bayesian quantile regression methods (2010, positive): “Yu and Moyeed (2001) propose Bayesian inference about quantile regressions using as a likelihood an asymmetric Laplace distribution for the error term u in a linear model of the form p(u) ∝ exp{−ρτ (u)} where ρτ (u) is the check function ρτ (u) = u(τ − 1(u ≤ 0)) and u = Y −X ′βτ .”
Bayesian Quantile Regression for Ordinal Models (2016, methodology): “The Bayesian method of estimating quantile regression uses the fact that maximization of the likelihood, where the error follows an AL distribution, is equivalent to minimization of the quantile objective function (Yu and Moyeed, 2001).”
Gibbs sampling methods for Bayesian quantile regression (2011, methodology): “Therefore, Yu and Moyeed [9] considered Markov chain Monte Carlo (MCMC) methods for posterior inference.”
Generating and Calibrating Probabilistic Quantitative Precipitation Forecasts from the High-Resolution NWP Model COSMO-DE (2012, methodology): “QR can also be formulated in terms of a linear regression model using the asymmetric Laplace distribution (Yu and Moyeed 2001). a. Generalized linear models GLMs extend classical linear regression models on the basis of two assumptions.”