Design of R control Chart Assuming a Gamma Distribution

2001 ◽  
Vol 16 (2) ◽  
Author(s):  
I. M. González ◽  
E. Viles
Keyword(s):  
Control Chart ◽  
Gamma Distribution

scholarly journals New Control Charts for a Multivariate Gamma Distribution

2021 ◽  
pp. 607-614
Author(s):  
Hamzeh Torabi ◽  
Shohreh Enami ◽  
STA Niaki
Keyword(s):  
Control Chart ◽  
Gamma Distribution ◽  
Control Charts ◽  
Normal Approximation ◽  
Average Run Length ◽  
Exact Distribution ◽  
Run Length ◽  
Multivariate Gamma Distribution ◽  
Satterthwaite Approximation

In this study, a multivariate gamma distribution is first introduced. Then, by defining a new statistic, three control charts called the MG charts, are proposed for this distribution. The first control chart is based on the exact distribution of this statistic, the second control chart is based on the Satterthwaite approximation, and the last is based on the normal approximation. Efficiency of the proposed control charts is evaluated by average run length (ARL) criterion.

scholarly journals A Control Chart for Gamma Distribution using Multiple Dependent State Sampling

2017 ◽  
Vol 16 (1) ◽  
pp. 109-117 ◽  
Author(s):  
Muhammad Aslam ◽  
Osama-H. Arif ◽  
Chi-Hyuck Jun
Keyword(s):  
Control Chart ◽  
Gamma Distribution ◽  
Dependent State

scholarly journals Monitoring the Process Based on Belief Statistic for Neutrosophic Gamma Distributed Product

Processes ◽  
2019 ◽  
Vol 7 (4) ◽  
pp. 209 ◽  
Author(s):  
Muhammad Aslam ◽  
Rashad A. R. Bantan ◽  
Nasrullah Khan
Keyword(s):  
Control Chart ◽  
Gamma Distribution ◽  
Average Run Length ◽  
Simulation Data ◽  
Run Length ◽  
Classical Statistics ◽  
The Mean ◽  
Uncertainty Environment ◽  
Mean Time

In this paper, we developed a control chart methodology for the monitoring the mean time between two events using the belief estimator under the neutrosophic gamma distribution. The proposed control chart coefficients and the neutrosophic average run length (NARL) have been determined using different process settings. The performance of the proposed chart is compared with the control chart under classical statistics in terms of NARL using the simulation data and real example. From comparisons, it is concluded that the proposed chart is efficient, effective and adequate to be used under uncertainty environment than the chart under classical statistics.

A control chart for the Gamma distribution as a model of time between events

2007 ◽  
Vol 45 (23) ◽  
pp. 5649-5666 ◽  
Author(s):  
C. W. Zhang ◽  
M. Xie ◽  
J. Y. Liu ◽  
T. N. Goh
Keyword(s):  
Control Chart ◽  
Gamma Distribution
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