The Robustness and Performance of CUSUM Control Charts Based on the Double-Exponential and Normal Distributions

2004 ◽  
pp. 79-100 ◽  
Author(s):  
Zachary G. Stoumbos ◽  
Marion R. Reynolds
Keyword(s):  
Control Charts ◽  
Normal Distributions ◽  
Double Exponential ◽  
And Performance ◽  
Cusum Control Charts

scholarly journals X̅ and R control charts based on marshall-olkin inverse log-logistic distribution for positive skewed data

2020 ◽  
Vol 1 (1) ◽  
pp. 9-16
Author(s):  
O. L. Aako ◽  
J. A. Adewara ◽  
K. S Adekeye ◽  
E. B. Nkemnole
Keyword(s):  
Control Chart ◽  
Control Charts ◽  
Average Run Length ◽  
Logistic Distribution ◽  
Skewed Data ◽  
Process Data ◽  
And Performance ◽  
Set Up ◽  
Control Limits ◽  
Normally Distributed

The fundamental assumption of variable control charts is that the data are normally distributed and spread randomly about the mean. Process data are not always normally distributed, hence there is need to set up appropriate control charts that gives accurate control limits to monitor processes that are skewed. In this study Shewhart-type control charts for monitoring positively skewed data that are assumed to be from Marshall-Olkin Inverse Loglogistic Distribution (MOILLD) was developed. Average Run Length (ARL) and Control Limits Interval (CLI) were adopted to assess the stability and performance of the MOILLD control chart. The results obtained were compared with Classical Shewhart (CS) and Skewness Correction (SC) control charts using the ARL and CLI. It was discovered that the control charts based on MOILLD performed better and are more stable compare to CS and SC control charts. It is therefore recommended that for positively skewed data, a Marshall-Olkin Inverse Loglogistic Distribution based control chart will be more appropriate.

Design of CuSum Control Charts

1979 ◽  
pp. 76-99
Author(s):  
J. Murdoch
Keyword(s):  
Control Charts ◽  
Cusum Control Charts

Process capability and stability of analytical systems assessed from proficiency testing data

1994 ◽  
Vol 40 (5) ◽  
pp. 723-728 ◽  
Author(s):  
R W Jenny
Keyword(s):  
Proficiency Testing ◽  
Control Charts ◽  
Total Quality ◽  
Statistical Process ◽  
Laboratory Performance ◽  
Continuous Quality ◽  
Valuable Adjunct ◽  
Capability Analysis ◽  
Performance Specifications ◽  
And Performance

Abstract Participation in a proficiency testing (PT) program is a valuable adjunct to laboratory activities dedicated to the maintenance of reliable analytical methods. The PT program may facilitate continuous quality improvement if laboratory performance is presented in the context of expectations espoused by healthcare professionals for optimal patient care. Statistical process control (SPC) and capability analysis are tools used by industry in a Total Quality Management environment to characterize and monitor the performance of its processes relative to performance specifications. I conceptualized the use of an analytical system by many laboratories as a process that periodically produces results from the analysis of PT specimens. I treated a set of five PT results (theophylline) reported by a laboratory as a process sample and subjected the samples collected from many laboratories to SPC and capability analysis. The control charts--mean-(X-bar) and s-charts--produced by the analysis readily identify significant analytical errors in the context of peer performance and performance specifications provided by the regulatory program and analytical goal setting. The capability index (desirable Cp > 1.0) determined from clinical specification limits for the three analytical systems evaluated suggests an opportunity for improvement of laboratory performance.

CUSUM control charts based on likelihood ratio for preliminary analysis

2007 ◽  
Vol 50 (1) ◽  
pp. 47-62
Author(s):  
Yi Dai ◽  
Zhao-jun Wang ◽  
Chang-liang Zou
Keyword(s):  
Likelihood Ratio ◽  
Control Charts ◽  
Preliminary Analysis ◽  
Cusum Control Charts

Nonparametric multivariate CUSUM control charts for location and scale changes

2013 ◽  
Vol 25 (1) ◽  
pp. 1-20 ◽  
Author(s):  
Jun Li ◽  
Xin Zhang ◽  
Daniel R. Jeske
Keyword(s):  
Control Charts ◽  
Cusum Control Charts
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