Economically Designed Bayesian np Control Charts Using Dual Sample Sizes for Long-Run Processes

2019 ◽  
pp. 219-232
Author(s):  
Imen Kooli ◽  
Mohamed Limam
Keyword(s):  
Control Charts ◽  
Sample Sizes ◽  
Long Run

Control Charts for Poisson Count Data with Varying Sample Sizes

2010 ◽  
Vol 42 (3) ◽  
pp. 260-275 ◽  
Author(s):  
Anne G. Ryan ◽  
William H. Woodall
Keyword(s):  
Count Data ◽  
Control Charts ◽  
Sample Sizes ◽  
Poisson Count

Attributes Control Charts with Large Sample Sizes

1996 ◽  
Vol 28 (4) ◽  
pp. 451-459 ◽  
Author(s):  
Peter A. Heimann
Keyword(s):  
Control Charts ◽  
Sample Sizes ◽  
Large Sample

EWMA control charts with variable sample sizes and variable sampling intervals

2001 ◽  
Vol 33 (6) ◽  
pp. 511-530 ◽  
Author(s):  
MARION R. REYNOLDS ◽  
JESSE C. ARNOLD
Keyword(s):  
Control Charts ◽  
Sample Sizes ◽  
Variable Sampling ◽  
Sampling Intervals

Weighted CUSUM Control Charts for Monitoring Poisson Processes with Varying Sample Sizes

2011 ◽  
Vol 43 (4) ◽  
pp. 346-362 ◽  
Author(s):  
Wei Jiang ◽  
Lianjie Shu ◽  
Kwok-Leung Tsui
Keyword(s):  
Control Charts ◽  
Poisson Processes ◽  
Sample Sizes ◽  
Cusum Control Charts

scholarly journals A Study on the X ¯ and S Control Charts with Unequal Sample Sizes

Mathematics ◽  
2020 ◽  
Vol 8 (5) ◽  
pp. 698
Author(s):  
Chanseok Park ◽  
Min Wang
Keyword(s):  
Sample Size ◽  
Control Charts ◽  
Weighted Average ◽  
Missing Observations ◽  
Population Parameters ◽  
Sample Sizes ◽  
Cost Constraints ◽  
The Mean ◽  
True Population ◽  
Average Sample

The control charts based on X ¯ and S are widely used to monitor the mean and variability of variables and can help quality engineers identify and investigate causes of the process variation. The usual requirement behind these control charts is that the sample sizes from the process are all equal, whereas this requirement may not be satisfied in practice due to missing observations, cost constraints, etc. To deal with this situation, several conventional methods were proposed. However, some methods based on weighted average approaches and an average sample size often result in degraded performance of the control charts because the adopted estimators are biased towards underestimating the true population parameters. These observations motivate us to investigate the existing methods with rigorous proofs and we provide a guideline to practitioners for the best selection to construct the X ¯ and S control charts when the sample sizes are not equal.

CUSUM Control Charts with Variable Sample Sizes and Sampling Intervals

2001 ◽  
Vol 33 (1) ◽  
pp. 66-81 ◽  
Author(s):  
Jesse C. Arnold ◽  
Marion R. Reynolds
Keyword(s):  
Control Charts ◽  
Sample Sizes ◽  
Sampling Intervals ◽  
Cusum Control Charts

scholarly journals Monitoring Aggregated Poisson Data for Processes with Time-Varying Sample Sizes

2017 ◽  
Vol 40 (2) ◽  
pp. 243-262 ◽  
Author(s):  
Victor Hugo Morales ◽  
José Alberto Vargas
Keyword(s):  
Sample Size ◽  
Data Aggregation ◽  
Control Charts ◽  
Average Run Length ◽  
Health Surveillance ◽  
Occurrence Rate ◽  
Time Varying ◽  
Sample Sizes ◽  
Time Period ◽  
Poisson Data

This article deals with the effect of data aggregation, when Poisson processes with varying sample sizes, are monitored. These aggregation procedures are necessary or convenient in many applications, and can simplify monitoring processes. In health surveillance applications it is a common practice to aggregate the observations during a certain time period and monitor the processes at the end of it. Also, in this type of applications it is very frequent that the sample size vary over time, which makes that instead of monitor the mean of the processes, as would be in the case of Poisson observations with constant sample size, the occurrence rate of an adverse event is monitored.Two control charts for monitoring the count Poisson data with time-varying sample sizes are proposed by Shen et al. (2013) and Dong et al. (2008). We use the average run length (ARL) to compare the performance of these control charts when different levels of aggregation, two scenarios of generating of sample size and different out-of-control states are considered. Simulation studies show the effect of data aggregation in some situations, as well as those in which their use may be appropriate without significantly compromising the prompt detection of out-of-control signals. We also show the effect of data aggregation with an example of application in health surveillance.

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