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Online updating method to correct for measurement error in big data streams

Author

Listed:
  • Lee, JooChul
  • Wang, HaiYing
  • Schifano, Elizabeth D.

Abstract

When huge amounts of data arrive in streams, online updating is an important method to alleviate both computational and data storage issues. The scope of previous research for online updating is extended in the context of the classical linear measurement error model. In the case where some covariates are unknowingly measured with error at the beginning of the stream, but then are measured without error after a particular point along the data stream, the updated estimators ignoring the measurement error are biased for the true parameters. Once the covariates measured without error are first observed, a method to correct the bias of the estimators, as well as to correct the biases in their variance estimator, is proposed; after correction, the traditional online updating method can then proceed as usual. Further, asymptotic distributions for the corrected and updated estimators are established. Simulation studies and a real data analysis with an airline on-time dataset are provided to illustrate the performance of the proposed method.

Suggested Citation

  • Lee, JooChul & Wang, HaiYing & Schifano, Elizabeth D., 2020. "Online updating method to correct for measurement error in big data streams," Computational Statistics & Data Analysis, Elsevier, vol. 149(C).
    • Handle: RePEc:eee:csdana:v:149:y:2020:i:c:s0167947320300670
    DOI: 10.1016/j.csda.2020.106976
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    References listed on IDEAS

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    Cited by:

    1. Lee, JooChul & Schifano, Elizabeth D. & Wang, HaiYing, 2024. "Fast Optimal Subsampling Probability Approximation for Generalized Linear Models," Econometrics and Statistics, Elsevier, vol. 29(C), pages 224-237.

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