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Quantile regression for linear models with autoregressive errors using EM algorithm

Author

Listed:
  • Yuzhu Tian

    (Henan University of Science and Technology
    The Central University of Finance and Economics)

  • Manlai Tang

    (Hang Seng Management College)

  • Yanchao Zang

    (Henan University of Science and Technology)

  • Maozai Tian

    (Renmin University of China)

Abstract

In this paper, we consider the quantile linear regression models with autoregressive errors. By incorporating the expectation–maximization algorithm into the considered model, the iterative weighted least square estimators for quantile regression parameters and autoregressive parameters are derived. Finally, the proposed procedure is illustrated by simulations and a real data example.

Suggested Citation

  • Yuzhu Tian & Manlai Tang & Yanchao Zang & Maozai Tian, 2018. "Quantile regression for linear models with autoregressive errors using EM algorithm," Computational Statistics, Springer, vol. 33(4), pages 1605-1625, December.
    • Handle: RePEc:spr:compst:v:33:y:2018:i:4:d:10.1007_s00180-018-0811-1
    DOI: 10.1007/s00180-018-0811-1
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    References listed on IDEAS

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