Open AccessNonparametric Double EWMA Control Charts based on Mood Statistic for Process Variability
Author(s) -
Kanita Petcharat
Publication year - 2021
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/2014/1/012008
Subject(s) - ewma chart , control chart , statistics , nonparametric statistics , statistic , mathematics , chart , monte carlo method , statistical process control , computer science , process (computing) , operating system
This research aims to propose a nonparametric double exponentially weighted moving average (NDEWMA) control chart for detecting shifts in a process using the Mood statistic. A Monte Carlo simulation study was used. The data had a normal distribution and a non-normal distribution where the magnitude of shift size ( δ ) was δ = 1, 1.05, 1.10 - 2.00 and 3.00, respectively. A performance comparison of the control charts was evaluated by using the Average Run Length (ARL). In the case of an in-control process, a large ARL value is desired, while a small ARL value is desired when the process is out-of-control. From the results of the simulation study, it was observed that the proposed NDEWMA control chart is effective in detecting small shifts in the process and gives better performance compared to the nonparametric exponentially weighted moving average control (NEWMA) control chart based on Mood statistics.