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Bayesian expectile regression with asymmetric normal distribution

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  • Ji-Ji Xing
  • Xi-Yuan Qian

Abstract

In this paper, we adopt the Bayesian approach to expectile regression employing a likelihood function that is based on an asymmetric normal distribution. We demonstrate that improper uniform priors for the unknown model parameters yield a proper joint posterior. Three simulated data sets were generated to evaluate the proposed method which show that Bayesian expectile regression performs well and has different characteristics comparing with Bayesian quantile regression. We also apply this approach into two real data analysis.

Suggested Citation

  • Ji-Ji Xing & Xi-Yuan Qian, 2017. "Bayesian expectile regression with asymmetric normal distribution," Communications in Statistics - Theory and Methods, Taylor & Francis Journals, vol. 46(9), pages 4545-4555, May.
    • Handle: RePEc:taf:lstaxx:v:46:y:2017:i:9:p:4545-4555
    DOI: 10.1080/03610926.2015.1088030
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    Cited by:

    1. Henry R. Scharf & Xinyi Lu & Perry J. Williams & Mevin B. Hooten, 2022. "Constructing Flexible, Identifiable and Interpretable Statistical Models for Binary Data," International Statistical Review, International Statistical Institute, vol. 90(2), pages 328-345, August.

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