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Adaptive quantile regressions for massive datasets

Author

Listed:
  • Rong Jiang

    (Donghua University)

  • Wei-wei Chen

    (Donghua University)

  • Xin Liu

    (Donghua University)

Abstract

Analysis of massive datasets is challenging owing to limitations of computer primary memory. Adaptive quantile regressions is a robust and efficient estimation method. For computational efficiency, we propose an adaptive smoothing quantile regressions (ASQR). The ASQR method is used to analyze massive datasets. The proposed approach significantly reduces the required amount of primary memory, and the resulting estimate will be as efficient as if the entire data set is analyzed simultaneously. Both simulations and data analysis are conducted to illustrate the finite sample performance of the proposed methods.

Suggested Citation

  • Rong Jiang & Wei-wei Chen & Xin Liu, 2021. "Adaptive quantile regressions for massive datasets," Statistical Papers, Springer, vol. 62(4), pages 1981-1995, August.
    • Handle: RePEc:spr:stpapr:v:62:y:2021:i:4:d:10.1007_s00362-020-01170-8
    DOI: 10.1007/s00362-020-01170-8
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    References listed on IDEAS

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