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Communication-efficient distributed M-estimation with missing data

Author

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  • Shi, Jianwei
  • Qin, Guoyou
  • Zhu, Huichen
  • Zhu, Zhongyi

Abstract

In the big data era, practical applications often encounter incomplete data. Current distributed methods, ignoring missingness, may cause inconsistent estimates. Motivated by that, a distributed algorithm is developed for M-estimation with missing data. The proposed algorithm is communication-efficient, where only gradient information is transferred to the central machine. The parameters of interest and the nuisance parameters are simultaneously updated. Theoretically, it is shown that the proposed algorithm achieves a full sample performance after a moderate number of iterations. The influence of nuisance parameters on distributed M-estimation is also investigated. Simulations via synthetic data illustrate the effectiveness of the algorithm. At last, the algorithm is applied to a real data set.

Suggested Citation

  • Shi, Jianwei & Qin, Guoyou & Zhu, Huichen & Zhu, Zhongyi, 2021. "Communication-efficient distributed M-estimation with missing data," Computational Statistics & Data Analysis, Elsevier, vol. 161(C).
    • Handle: RePEc:eee:csdana:v:161:y:2021:i:c:s0167947321000852
    DOI: 10.1016/j.csda.2021.107251
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    References listed on IDEAS

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