Quantile Regression via the EM Algorithm

DOI10.1080/03610918.2012.746980zbMath1462.62205OpenAlexW2073259699MaRDI QIDQ2876134

Yong Li, Yinghui Zhou, Zhongxin Ni

Publication date: 18 August 2014

Published in: Communications in Statistics - Simulation and Computation (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1080/03610918.2012.746980

zbMATH Keywords

asymmetric Laplace distributionMM algorithmgeneralized inverse Gaussian distributiongeneralized EM algorithmnormal-variance--mean mixture

Mathematics Subject Classification ID

Nonparametric regression and quantile regression (62G08)

Related Items (7)

Estimation of linear composite quantile regression using EM algorithm ⋮ Gibbs sampling for mixture quantile regression based on asymmetric Laplace distribution ⋮ Maximum likelihood estimation for quantile autoregression models with Markovian switching ⋮ A non-iterative posterior sampling algorithm for linear quantile regression model ⋮ Logistic quantile regression for bounded outcomes using a family of heavy-tailed distributions ⋮ Markov switching quantile regression models with time-varying transition probabilities ⋮ Quantile regression via the EM algorithm for joint modeling of mixed discrete and continuous data based on Gaussian copula

Cites Work

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• On the convergence properties of the EM algorithm
• An EM type algorithm for maximum likelihood estimation of the normal-inverse Gaussian distribution
• An interior point algorithm for nonlinear quantile regression
• Quantile regression for longitudinal data using the asymmetric Laplace distribution
• The EM Algorithm and Extensions, 2E
• Normal Variance-Mean Mixtures and z Distributions
• Regression Quantiles
• Improving the EM Algorithm
• Gibbs sampling methods for Bayesian quantile regression
• A Three-Parameter Asymmetric Laplace Distribution and Its Extension
• Bayesian quantile regression

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