Results 251 to 260 of about 416,034 (304)

A Note on Average Run Lengths of Moving Average Control Charts

Economic Quality Control, 2004
Using a counter example, we show that the formula for computing the Average Run Lengths (ARL) of a moving average control chart (MA) in Wetherill and Brown (1991) is incorrect. However, we conjecture that the formula may provide an upper bound for the ARL of a moving average control chart.
Lingyun Zhang, C. D. Lai, K. Govindaraju, M. Bebbington   +3 more
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Computing Average Run Lengths for the MaxEWMA Chart

Communications in Statistics - Simulation and Computation, 2004
The MaxEWMA chart has recently been introduced as an alternative to control charting procedures that are designed to jointly detect changes in the mean and standard deviation of a normally distributed process. Here, a method for computing both in-control and out-of-control average run lengths for purposes of effectively designing this chart is offered.
Maria E. Calzada, Stephen M. Scariano, Gemai Chen   +2 more
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Average Run Lengths of Geometric Moving Average Charts by Numerical Methods

Technometrics, 1978
A numerical procedure is presented for the tabulation of average run lengths (ARL's) of geometric moving average charts. Both one-and two-sided ARL's are given for various settings of the control limits, smoothing constant and shift in the nominal level of the process mean. Where comparison is possible.
P. B. Robinson, T. Y. Ho
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AVERAGE RUN LENGTHS FOR MOVING AVERAGE CONTROL CHARTS

Probability in the Engineering and Informational Sciences, 1999
We are interested in E [N], the mean time until the most recent k values of a sequence of independent and identically distributed random variables exceeds a specified constant. Using recent results, we present a simulation procedure for determining E [N]. These results are also used to obtain upper and lower bounds for E [N]. These bounds, however,
openaire   +1 more source

Study of average run lengths for supplementary runs rules in the presence of autocorrelation

Communications in Statistics - Simulation and Computation, 1994
The basic assumption underlying statistical control chart criteria is that the process measurements are independent and identically distributed over time. However, autocorrelation and other time-series effects occur frequently in application. In this paper, the effects of autocorrelation are investigated for the frequently advocated supplementary runs ...
Alwan, Layth C., Champ, Charles W., Maragah, Hazem D.   +2 more
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Average run length when monitoring capability indices using EWMA

Quality and Reliability Engineering International, 2008
AbstractIn order to monitor unstable but capable processes Castagliola and Vännman have recently suggested a procedure based on an EWMA approach, called EWMA capability chart, for monitoring Vännman's Cp(u,v)‐family of capability indices and showed how their proposed approach efficiently monitors capable processes by detecting a decrease or increase in
Castagliola, Philippe, Vännman, Kerstin
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Computing Average Run Lengths of Exponential EWMA Charts

Journal of Quality Technology, 2000
An exponential EWMA chart is useful for monitoring the mean β of interarrival times of nonconforming items in many manufacturing processes.
Gan, F.F., Chang, T.C.
openaire   +1 more source

Average Run Length Computations for the Three-Way Chart

Communications in Statistics - Simulation and Computation, 2004
An integral equation for the average run length of the Three-Way control chart is analytically derived, and a numerical method for solving it is presented. Useful tables of the results of run length computations are provided for shifts in the process mean and standard deviation.
Maria E. Calzada, Stephen M. Scariano
openaire   +1 more source

Average Run Lengths of Exponentially Weighted Moving Average Control Charts

Journal of Quality Technology, 1987
A computer program is presented that calculates the average run length of exponentially weighted moving average charts for controlling the mean of a normal process...
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Average run length comparison of multivariate control charts

Journal of Statistical Computation and Simulation, 2001
In this paper we use Monte Carlo Simulation methodology to compare the effectiveness of five multivariate quality control methods, namely Hotelling T 2, Multivariate Shewhart Char, Discriminant Analysis, Decomposition Method, and Multivariate Ridge Residual Chart-developed by Authors-, for controlling the mean vector in a multivariate process.
Amir Javaheri, Ali A. Houshmand
openaire   +1 more source