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The Expectation–Maximization approach for Bayesian quantile regression

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  • Zhao, Kaifeng
  • Lian, Heng

Abstract

This paper deals with Bayesian linear quantile regression models based on a recently developed Expectation–Maximization Variable Selection (EMVS) method. By using additional latent variables, the proposed approach enjoys enormous computational savings compared to commonly used Markov Chain Monte Carlo (MCMC) algorithm. Using location-scale mixture representation of asymmetric Laplace distribution (ALD), we develop a rapid and efficient Expectation–Maximization (EM) algorithm, which is illustrated with several carefully designed simulation examples. We further apply the proposed method to construct financial index tracking portfolios.

Suggested Citation

  • Zhao, Kaifeng & Lian, Heng, 2016. "The Expectation–Maximization approach for Bayesian quantile regression," Computational Statistics & Data Analysis, Elsevier, vol. 96(C), pages 1-11.
    • Handle: RePEc:eee:csdana:v:96:y:2016:i:c:p:1-11
    DOI: 10.1016/j.csda.2015.11.005
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    References listed on IDEAS

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    Cited by:

    1. Julio Cezar Soares Silva & Adiel Teixeira de Almeida Filho, 2023. "A systematic literature review on solution approaches for the index tracking problem in the last decade," Papers 2306.01660, arXiv.org, revised Jun 2023.
    2. Matthew D. Koslovsky & Michael D. Swartz & Wenyaw Chan & Luis Leon†Novelo & Anna V. Wilkinson & Darla E. Kendzor & Michael S. Businelle, 2018. "Bayesian variable selection for multistate Markov models with interval†censored data in an ecological momentary assessment study of smoking cessation," Biometrics, The International Biometric Society, vol. 74(2), pages 636-644, June.

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