scholarly journals A Repetitive Sampling-based Control Chart for Multivariate Weighed Poisson Distribution with Two Different Indexes‎

2019 ◽  
Vol 16 (1) ◽  
pp. 245-254
Author(s):  
Shohreh Enami ◽  
Hamzeh Torabi ◽  
◽  
Keyword(s):  
Poisson Distribution ◽  
Control Chart

On the Estimation Error in Zero-Inflated Poisson Model for Process Control

2003 ◽  
Vol 10 (02) ◽  
pp. 159-169 ◽  
Author(s):  
B. He ◽  
M. Xie ◽  
T. N. Goh ◽  
P. Ranjan
Keyword(s):  
Sample Size ◽  
Poisson Distribution ◽  
Control Chart ◽  
Average Run Length ◽  
Estimation Error ◽  
Accurate Estimate ◽  
Minimum Size ◽  
High Quality ◽  
Run Length ◽  
Run Length Distribution

The control chart based on a Poisson distribution has often been used to monitor the number of defects in sampling units. However, many false alarms could be observed due to extra zero counts, especially for high-quality processes. Therefore, some alternatives have been developed to alleviate this problem, one of which is the control chart based on the zero-inflated Poisson distribution. This distribution takes into account the extra zeros present in the data, and yield more accurate results than the Poisson distribution. However, implementing a control chart is often based on the assumption that the parameters are either known or an accurate estimate is available. For a high quality process, an accurate estimate may require a very large sample size, which is seldom available. In this paper the effect of estimation error is investigated. An analytical approximation is derived to compute shift detection probability and run length distribution. The study shows that the false alarm rates are higher than the desirable level for smaller values of the sample size. This is further supported by smaller average run length. In general, the quantitative results from this paper can be utilized to select a minimum size of the initial sample for estimating the control limits so that certain average run length requirements are met.

A Control Chart for COM-Poisson Distribution Using Multiple Dependent State Sampling

2016 ◽  
Vol 32 (8) ◽  
pp. 2803-2812 ◽  
Author(s):  
Muhammad Aslam ◽  
Liaquat Ahmad ◽  
Chi-Hyuck Jun ◽  
Osama H. Arif
Keyword(s):  
Poisson Distribution ◽  
Control Chart ◽  
Dependent State

scholarly journals A Control Chart Based on Moving Average Model Functioned for Poisson Distribution

2020 ◽  
Vol 3 (10) ◽  
Author(s):  
Hira Arooj ◽  
◽  
Khawar Iqbal Malik ◽  
Keyword(s):  
Poisson Distribution ◽  
Control Chart ◽  
Control Charts ◽  
Moving Average ◽  
Current Control ◽  
Average Model ◽  
Time Intervals ◽  
Manufacture Process ◽  
Moving Average Model

A control chart used with MA control chart to track the number of faulty products or faults suggested. When the characteristics of quality of interest obey a Poisson distribution. A specified number of objects are observed at various time intervals in order to observe the number of non-conformities. By measuring ARLs under different sample sizes and parameters by considering ARLs in power, the output of the proposed chart is evaluated. It should be noted The proposed control chart seems to be morereliable than the traditional current control charts in detecting small adjustments in the manufacture process.

scholarly journals Design of a Control Chart Based on COM-Poisson Distribution for the Uncertainty Environment

Complexity ◽  
2019 ◽  
Vol 2019 ◽  
pp. 1-9
Author(s):  
Muhammad Aslam ◽  
Ali Hussein Al-Marshadi
Keyword(s):  
Poisson Distribution ◽  
Control Chart ◽  
Simulation Study ◽  
Average Run Length ◽  
Interval Method ◽  
Interval System ◽  
Run Length ◽  
Classical Statistics ◽  
Uncertainty Environment

This paper will introduce the neutrosophic COM-Poisson (NCOM-Poisson) distribution. Then, the design of the attribute control chart using the NCOM-Poisson distribution is given. The structure of the control chart under the neutrosophic statistical interval method will be given. The algorithm to determine the average run length under neutrosophic statistical interval system will be given. The performance of the proposed control chart is compared with the chart based on classical statistics in terms of neutrosophic average run length (NARL). A simulation study and a real example are also added. From the comparison of the proposed control chart with the existing chart, it is concluded that the proposed control chart is more efficient in detecting a shift in the process. Therefore, the proposed control chart will be helpful in minimizing the defective product. In addition, the proposed control chart is more adequate and effective to apply in uncertainty environment.

A control chart for multivariate Poisson distribution using repetitive sampling

2016 ◽  
Vol 44 (1) ◽  
pp. 123-136 ◽  
Author(s):  
Muhammad Aslam ◽  
G. Srinivasa Rao ◽  
Liaquat Ahmad ◽  
Chi-Hyuck Jun
Keyword(s):  
Poisson Distribution ◽  
Control Chart ◽  
Multivariate Poisson Distribution

A hybrid exponentially weighted moving average chart for COM-Poisson distribution

2016 ◽  
Vol 40 (2) ◽  
pp. 456-461 ◽  
Author(s):  
Muhammad Aslam ◽  
Nasrullah Khan ◽  
Chi-Hyuck Jun
Keyword(s):  
New York ◽  
Poisson Distribution ◽  
Control Chart ◽  
Control Charts ◽  
Moving Average ◽  
Statistical Quality Control ◽  
Exponentially Weighted Moving Average ◽  
Run Lengths ◽  
Weighted Moving Average ◽  
Exponentially Weighted

We provide the complete design of a hybrid exponentially weighted moving average (HEWMA) control chart for COM-Poisson distribution. The necessary measures of the proposed control chart are given in this manuscript, and the average run lengths (ARLs) are determined through Monte Carlo simulation for various values of specified parameters. The performance of the proposed chart is compared with two existing control charts. The proposed chart is more efficient than these two existing charts in terms of ARLs; application of the proposed chart is described with the help of Montgomery’s data ( Introduction to Statistical Quality Control, John Wiley & Sons, New York, 2007).

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