Robust Monitoring of Contaminated Multivariate Data | Zendy

Eric B. Howington | Zendy

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Robust Monitoring of Contaminated Multivariate Data

Author(s) -

Eric B. Howington

Publication year - 2013

Publication title -

advances in decision sciences

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.178

H-Index - 13

eISSN - 2090-3367

pISSN - 2090-3359

DOI - 10.1155/2013/961501

Subject(s) - control chart , outlier , multivariate statistics , statistic , computer science , chart , contamination , statistics , multivariate analysis , ewma chart , control limits , data mining , process (computing) , artificial intelligence , mathematics , ecology , biology , operating system

Monitoring a process that suffers from data contamination using a traditional multivariate T2 chart can lead to an excessive number of false alarms. A diagnostic statistic can be used to distinguish between real control chart signals due to assignable causes and signals due to contamination from a single outlier. In phase II analysis, a traditional T2 control chart augmented by a diagnostic statistic improves the work stoppage rates for multivariate processes suffering from contaminated data and maintains the ability to detect process shifts

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