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Yasuhiro Omori, N. Johnson, S. Kotz, N. Balakrishnan (1995)
Continuous Univariate Distributions.Journal of the American Statistical Association, 90
C. Borror, D. Montgomery, G. Runger (1999)
Robustness of the EWMA Control Chart to Non-NormalityJournal of Quality Technology, 31
D. Montgomery (1985)
Introduction to Statistical Quality Control
G. Fishman, D. Gross (1976)
Concepts and Methods in Discrete Event Digital SimulationIEEE Transactions on Systems, Man, and Cybernetics, 6
F. Hampel, E. Ronchetti, P. Rousseeuw, W. Stahel (1986)
Robust statistics: the approach based on influence functions
David Rocke (1989)
Robust control chartsTechnometrics, 31
F. Hampel (1971)
A General Qualitative Definition of RobustnessAnnals of Mathematical Statistics, 42
H. Shore (2001)
Process Control for Non-Normal Populations Based on an Inverse Normalizing Transformation
B. Efron (1979)
Bootstrap Methods: Another Look at the JackknifeAnnals of Statistics, 7
M. Cox, E. Igúzquiza (2001)
THE TOTAL MEDIAN AND ITS UNCERTAINTY
Z. Stoumbos, M. Reynolds (2000)
Robustness to non-normality and autocorrelation of individuals control chartsJournal of Statistical Computation and Simulation, 66
T. Ryan, Belinda Faddy (2001)
The Effect of Non-Normality on the Performance of CUSUM Procedures
In industry, most of the process observations are assumed to come from a normal population, but usually we merely want to control the process mean value. It is thus sensible to find control statistics, which are ‘robust’ to monitor the process mean, giving the expected rate of false alarms whenever that mean is close to the target value, although not under a normal regime. Simulation studies for a few symmetric and asymmetric distributions allow us to suggest the total median as a robust median estimator. We shall here analyse such a robustness, as well as the robustness of the total median chart comparatively to the sample mean chart, whenever we want to control the mean value of a symmetric underlying parent. Some indication is also provided on the comparative out‐of‐control behaviour of the two charts. Copyright © 2004 John Wiley & Sons, Ltd.
Applied Stochastic Models in Business and Industry – Wiley
Published: Oct 1, 2004
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