Robust Multivariate Control Charts to Detect Small Shifts in Mean | Zendy

Habshah Midi | Zendy; Ashkan Shabbak | Zendy

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Robust Multivariate Control Charts to Detect Small Shifts in Mean

Author(s) -

Habshah Midi,

Ashkan Shabbak

Publication year - 2011

Publication title -

mathematical problems in engineering

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.262

H-Index - 62

eISSN - 1026-7077

pISSN - 1024-123X

DOI - 10.1155/2011/923463

Subject(s) - ewma chart , cusum , multivariate statistics , outlier , statistics , robustness (evolution) , multivariate analysis , control chart , computer science , mathematics , process (computing) , biochemistry , chemistry , gene , operating system

The classical multivariate CUSUM and EWMA charts are commonly used to detect small shifts in the mean vectors. It is now evident that those charts are easily affected by outliers which may be due to small or moderate changes in the mean vector. In this paper, we propose a robust multivariate CUSUM and Robust multivariate EWMA charts to remedy the problem of small changed in scatter outliers. Both the empirical and simulation results indicate that the proposed robust multivariate CUSUM and EWMA charts offer substantial improvement over other multivariate CUSUM and EWMA charts. This article also discussed the robustness of the proposed charts, when there is a small or moderate sustained shift in the data set

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