Online process mean estimation using L1-norm exponential smoothing

Online process mean estimation using L1-norm exponential smoothing

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Article ID: iaor200969166
Country: United States
Volume: 56
Issue: 5
Start Page Number: 439
End Page Number: 449
Publication Date: Aug 2009
Journal: Naval Research Logistics
Authors: ,
Keywords: exponential smoothing, l1-norm
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.

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