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An effective method to reduce the computational complexity of composite quantile regression

Author

Listed:
  • Yanke Wu

    (Guangdong Ocean University
    Renmin University of China)

  • Maozai Tian

    (Renmin University of China
    Lanzhou University of Finance and Economics
    Xinjiang University of Finance and Economics)

Abstract

In this article, we aim to reduce the computational complexity of the recently proposed composite quantile regression (CQR). We propose a new regression method called infinitely composite quantile regression (ICQR) to avoid the determination of the number of uniform quantile positions. Unlike the composite quantile regression, our proposed ICQR method allows combining continuous and infinite quantile positions. We show that the proposed ICQR criterion can be readily transformed into a linear programming problem. Furthermore, the computing time of the ICQR estimate is far less than that of the CQR, though it is slightly larger than that of the quantile regression. The oracle properties of the penalized ICQR are also provided. The simulations are conducted to compare different estimators. A real data analysis is used to illustrate the performance.

Suggested Citation

  • Yanke Wu & Maozai Tian, 2017. "An effective method to reduce the computational complexity of composite quantile regression," Computational Statistics, Springer, vol. 32(4), pages 1375-1393, December.
    • Handle: RePEc:spr:compst:v:32:y:2017:i:4:d:10.1007_s00180-017-0749-8
    DOI: 10.1007/s00180-017-0749-8
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    References listed on IDEAS

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