Renewable learning for multiplicative regression with streaming datasets

Tianzhen Wang, Haixiang Zhang () and Liuquan Sun
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Tianzhen Wang: Tianjin University
Haixiang Zhang: Tianjin University
Liuquan Sun: Chinese Academy of Sciences

Computational Statistics, 2024, vol. 39, issue 3, No 20, 1559-1586

Abstract: 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.

Keywords: Big data; Multiplicative regression; Positive responses; Renewable learning; Streaming data (search for similar items in EconPapers)
Date: 2024
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DOI: 10.1007/s00180-023-01360-6

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