An exponentially weighted moving average chart based on likelihood-ratio test for monitoring Weibull mean and variance with subgroups

2017 ◽  
Vol 33 (8) ◽  
pp. 2409-2421 ◽  
Author(s):  
Fu-Kwun Wang ◽  
Xiao-Bin Cheng
Keyword(s):  
Likelihood Ratio ◽  
Likelihood Ratio Test ◽  
Moving Average ◽  
Ratio Test ◽  
Exponentially Weighted Moving Average ◽  
Mean And Variance ◽  
Weighted Moving Average ◽  
Exponentially Weighted

scholarly journals Performance of Joint Quality Monitoring Schemes under Gaussian distribution

2020 ◽  
Vol 9 (2) ◽  
pp. 335-340
Keyword(s):  
Moving Average ◽  
Statistical Quality Control ◽  
Exponentially Weighted Moving Average ◽  
Cumulative Sum ◽  
Monitoring Scheme ◽  
Mean And Variance ◽  
Joint Monitoring ◽  
Mean Variance ◽  
Weighted Moving Average ◽  
Exponentially Weighted

Jointly monitoring the process mean and variance has become a well-known topic in statistical quality control literature after it is considered as a bivariate problem. Many joint monitoring schemes have been proposed by using the Shewhart, cumulative sum and exponentially weighted moving average techniques. In this paper, best performing schemes from each technique has been selected and compared for their performance using average run length properties. It was found that selection of better joint monitoring scheme based on the shift in mean and variance to be detected quickly. In particular, the Shewhart distance joint monitoring scheme performs well when there is larger shifts in mean, variance or in both. In addition, the Shewhart distance joint monitoring scheme performs specific when there is no shift in mean and decrease in variance. For the smaller shifts in mean, variance or in both, cumulative sum and exponentially weighted moving average joint monitoring schemes can be recommended. At this scenario exponentially weighted moving average joint monitoring scheme performs marginally better than the cumulative sum scheme.

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