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Cites:

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Information Journal Paper

Title

PHASE II MONITORING OF AUTOCORRELATED LINEAR PROFILES USING LINEAR MIXED MODEL

Pages

  1-9

Abstract

 In many circumstances, the quality of a process or product is best characterized by a given mathematical function between a response variable and one or more explanatory variables that is typically referred to as profile. There are some investigations to monitor auto correlated linear and nonlinear profiles in recent years. In the present paper, we use the LINEAR MIXED MODELs to account autocorrelation within observations which is gathered on phase II of the monitoring process. We undertake that the structure of correlated linear profiles simultaneously has both random and fixed effects. The work enhanced a Hotelling’s T2 statistic, a multivariate exponential weighted moving average (MEWMA), and a multivariate cumulative sum (MCUSUM) control charts to monitor process. We also compared their performances, in terms of AVERAGE RUN LENGTH criterion, and designated that the proposed control charts schemes could effectively act in detecting shifts in process parameters. Finally, the results are applied on a real case study in an agricultural field.

Cites

References

Cite

APA: Copy

NARVAND, A., SOLEIMANI, P., & RAISSI, SADIGH. (2013). PHASE II MONITORING OF AUTOCORRELATED LINEAR PROFILES USING LINEAR MIXED MODEL. JOURNAL OF INDUSTRIAL ENGINEERING INTERNATIONAL, 9(9), 1-9. SID. https://sid.ir/paper/309915/en

Vancouver: Copy

NARVAND A., SOLEIMANI P., RAISSI SADIGH. PHASE II MONITORING OF AUTOCORRELATED LINEAR PROFILES USING LINEAR MIXED MODEL. JOURNAL OF INDUSTRIAL ENGINEERING INTERNATIONAL[Internet]. 2013;9(9):1-9. Available from: https://sid.ir/paper/309915/en

IEEE: Copy

A. NARVAND, P. SOLEIMANI, and SADIGH RAISSI, “PHASE II MONITORING OF AUTOCORRELATED LINEAR PROFILES USING LINEAR MIXED MODEL,” JOURNAL OF INDUSTRIAL ENGINEERING INTERNATIONAL, vol. 9, no. 9, pp. 1–9, 2013, [Online]. Available: https://sid.ir/paper/309915/en

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