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<p>In this article, the authors explore the online updating estimation for general estimating equations (EEs) in heterogeneous streaming data settings. The framework is based on more conservative model assumptions, leading to more robust estimations and preventing misspecification. The authors establish the standard renewable estimation under blockwise heterogeneity assumption, which can correctly specify model in some sense. To mitigate heterogeneity and enhance estimation accuracy, the authors propose two novel online detection and fusion strategies, with corresponding algorithms provided. Theoretical properties of the proposed methods are demonstrated in the context of small block sizes. Extensive numerical experiments validate the theoretical findings. Real data analysis of the Ford Gobike docked bike-sharing dataset verifies the feasibility and robustness of the proposed methods.</p>

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Renewable Estimation and Heterogeneity Detection Under Heterogeneous Estimating Equation Settings

  • 期刊论文

  • 发表日期：

    2025年12月12日

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  • Shuailin Wang,
  • Lu Lin

摘要

In this article, the authors explore the online updating estimation for general estimating equations (EEs) in heterogeneous streaming data settings. The framework is based on more conservative model assumptions, leading to more robust estimations and preventing misspecification. The authors establish the standard renewable estimation under blockwise heterogeneity assumption, which can correctly specify model in some sense. To mitigate heterogeneity and enhance estimation accuracy, the authors propose two novel online detection and fusion strategies, with corresponding algorithms provided. Theoretical properties of the proposed methods are demonstrated in the context of small block sizes. Extensive numerical experiments validate the theoretical findings. Real data analysis of the Ford Gobike docked bike-sharing dataset verifies the feasibility and robustness of the proposed methods.