Bayesian Semiparametric Modelling in Quantile Regression

By Athanasios Kottas, Milovan Krnjajic. 2009

166 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/9a8a9372f92d8918a73e0bb42d372689cdd462d6

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Citations in the corpus

Bayesian Tail Risk Interdependence Using Quantile Regression (2015, methodology): “The Bayesian quantile regression approach (see Yu and Moyeed, 2001; Kottas and Gelfand, 2001; Kottas and Krnjajic, 2009; Sriram et al., 2013) instead considers the distribution of Y | x as belonging to the Asymmetric Laplace distribution family, denoted by ALD (τ, q (x) , σ), with positive σ, whose ”
A Bayesian Nonparametric Approach to Inference for Quantile Regression (2010, methodology): “…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 error distribution.”
Simultaneous Linear Quantile Regression: A Semiparametric Bayesian Approach (2012, methodology): “Subsequent Bayesian approaches have looked into more flexible formulations of fε(²), including non-parametric formulations with extensions to the heteroscedastic case: fε(² | X = x) (Kottas and Gelfand 2001; Gelfand and Kottas 2003; Kottas and Krnjajić 2009; Thompson et al. 2010).”
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).”
Beyond mean regression (2013, methodology): “Examples include Kottas and Krnjajic (2009) based on different Dirichlet process mixtures for the quantile-specific error distribution, Reich et al. (2010) utilising a joint location-scale model for all quantiles with linear predictors for mean and standard deviation and Dirichlet process mixture…”
Gibbs sampling methods for Bayesian quantile regression (2011, methodology): “Recently, Kottas and Krnjajic 11 developed Bayesian semi-parametric models for quantile regression using Dirichlet process mixtures for the error distribution.”