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Online process mean estimation using L1 norm exponential smoothing

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  • Wei Jiang
  • Yongzhong Zhu

Abstract

A basic assumption in process mean estimation is that all process data are clean. However, many sensor system measurements are often corrupted with outliers. Outliers are observations that do not follow the statistical distribution of the bulk of the data and consequently may lead to erroneous results with respect to statistical analysis and process control. Robust estimators of the current process mean are crucial to outlier detection, data cleaning, process monitoring, and other process features. This article proposes an outlier‐resistant mean estimator based on the L1 norm exponential smoothing (L1‐ES) method. The L1‐ES statistic is essentially model‐free and demonstrably superior to existing estimators. It has the following advantages: (1) it captures process dynamics (e.g., autocorrelation), (2) it is resistant to outliers, and (3) it is easy to implement. © 2009 Wiley Periodicals, Inc. Naval Research Logistics 2009

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  • Wei Jiang & Yongzhong Zhu, 2009. "Online process mean estimation using L1 norm exponential smoothing," Naval Research Logistics (NRL), John Wiley & Sons, vol. 56(5), pages 439-449, August.
    • Handle: RePEc:wly:navres:v:56:y:2009:i:5:p:439-449
    DOI: 10.1002/nav.20351
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