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Renewable learning for multiplicative regression with streaming datasets

Author

Listed:
  • Tianzhen Wang

    (Tianjin University)

  • Haixiang Zhang

    (Tianjin University)

  • Liuquan Sun

    (Chinese Academy of Sciences)

Abstract

When large amounts of data continuously arrive in streams, online updating is an effective way to reduce storage and computational burden. The key idea of online updating is that the previous estimators are sequentially updated only using the current data and summary statistics of historical data. In this article, we develop a renewable learning method for the multiplicative regression model with streaming data, where the parameter estimator based on a least product relative error (LPRE) criterion is renewed without revisiting historical data. Under some regularity conditions, we establish the consistency and asymptotic normality of the renewable estimator. Moreover, our proposed renewable estimator has an identical asymptotic distribution with that of the full data LPRE estimator. Numerical studies and two real-world datasets are provided to evaluate the performance of our proposed method.

Suggested Citation

  • Tianzhen Wang & Haixiang Zhang & Liuquan Sun, 2024. "Renewable learning for multiplicative regression with streaming datasets," Computational Statistics, Springer, vol. 39(3), pages 1559-1586, May.
    • Handle: RePEc:spr:compst:v:39:y:2024:i:3:d:10.1007_s00180-023-01360-6
    DOI: 10.1007/s00180-023-01360-6
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    References listed on IDEAS

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    1. Tianzhen Wang & Haixiang Zhang, 2022. "Optimal subsampling for multiplicative regression with massive data," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 76(4), pages 418-449, November.
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    7. Lan Luo & Peter X.‐K. Song, 2020. "Renewable estimation and incremental inference in generalized linear models with streaming data sets," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 82(1), pages 69-97, February.
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