Approximate Bayesian inference for quantiles

By David B. Dunson, Jack A. Taylor. 2005

107 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/3cc15883e5ef2125301de5997c67a02cb69cc39a

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

Bayesian quantile regression methods (2010, methodology): “Following Lavine (1995) and Dunson and Taylor (2005), we call this proposal Jeffreys’ substitution posterior or simply Jeffreys’ posterior.”
Bayesian Semiparametric Modelling in Quantile Regression (2009, unknown): “There are certain approaches that allow, in the context of (1) with h(x)=xT , simultaneous estimation for more than one quantile regression (see, e.g. Dunson & Taylor, 2005); however, this is only possible because they do not involve probabilistic modelling for the errors.”
A partially collapsed Gibbs sampler for Bayesian quantile regression (2009, methodology): “…for the regression model error (Kottas and Gelfand, 2001), an MCMC algorithm using Jeffreys’ (Jeffreys, 1961) substitution posterior for the median (Dunson and Taylor, 2005), the expectation-maximising (EM) algorithm using the asymmetric Laplace distribution (Geraci and Bottai, 2007), an…”
Bayesian quantile regression for longitudinal data models (2012, background): “Dunson and Taylor [5] discussed Bayesian inference for quantiles when the likelihood function is not fully specified.”
Inconsistency of Bayesian Inference for Misspecified Linear Models, and a Proposal for Repairing It (2014, background): “Extensive further discussion on Bayesian inference under misspecification is given by Walker (2013) and Grünwald and Langford (2007). For us, it remains to discuss the place of both the inconsistency result and our solution in Bayesian methodology.”
A Bayesian Nonparametric Approach to Inference for Quantile Regression (2010, methodology): “KEY WORDS: Dirichlet process mixture model; Markov chain Monte Carlo; Multivariate normal mixture; Tobit quantile regression.”
Posterior Inference in Bayesian Quantile Regression with Asymmetric Laplace Likelihood (2016, methodology): “Some researchers considered non-parametric working likelihoods, for example, the Dirichlet process mixture models in Gelfand & Kottas (2002) and Kottas & Krnjajić (2009), an infinite mixture of normals in Reich et al. (2010) and Jeffreys’ substitution likelihood in Dunson & Taylor (2005).”
Simultaneous Linear Quantile Regression: A Semiparametric Bayesian Approach (2012, background): “Therefore, the issue of simultaneous inference remains unsolved (see, however, Dunson and Taylor 2005; Lancaster and Jun 2010, who offer partial solutions based on pseudo and empirical likelihoods).”