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The Monitoring of Dirichlet Compositional Data

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@article{IJASEIT13429,
   author = {Reham W. Elshaer and Aya A. Aly},
   title = {The Monitoring of Dirichlet Compositional Data},
   journal = {International Journal on Advanced Science, Engineering and Information Technology},
   volume = {11},
   number = {5},
   year = {2021},
   pages = {1868--1875},
   keywords = {Compositional data; Dirichlet distribution; phase II monitoring; quality control; MEWMA chart.},
   abstract = {

Compositional data are used in many applications such as Cement, Asphalt, and many other Chemical industries. Such data represent random variables whose values must sum up to a certain constant. Quality engineers and technicians require monitoring compositional data and detecting the source of the irregularity in the process as soon as it happens. Throughout the literature, complicated methods were introduced to monitor compositional data. Such methods are computationally complex and can lead to difficulties in interpreting the results. The Dirichlet distribution is commonly used in the literature to model compositional data. In this study, we propose three simple methods to monitor the mean vector of the Dirichlet distribution. The first method is based on a MEWMA control chart. The second method is based on transforming the Dirichlet random variables into beta random variables and then monitoring them using multiple EWMA control charts, while the third method uses multiple EWMA control charts for transformed independent random variables. Using a simulation technique, the performance of the three methods is investigated, and the three methods performed very well under different sample sizes, many random variables, and values of the distribution parameters. When the process is out-of-control, the source of the out-of-control signal can be detected using Method 2 and Method 3. Method 2 maintained its good performance with a probability 0.99 of correctly detecting the source of the signal. Method 3 performed well except for the case of Dirichlet parameter values less than one. However, it maintained almost a probability of correct detection of at least 90% in most cases. The three proposed methods are simple, do not need complicated calculations, and can easily be applied and used by practitioners.

},    issn = {2088-5334},    publisher = {INSIGHT - Indonesian Society for Knowledge and Human Development},    url = {http://ijaseit.insightsociety.org/index.php?option=com_content&view=article&id=9&Itemid=1&article_id=13429},    doi = {10.18517/ijaseit.11.5.13429} }

EndNote

%A Elshaer, Reham W.
%A Aly, Aya A.
%D 2021
%T The Monitoring of Dirichlet Compositional Data
%B 2021
%9 Compositional data; Dirichlet distribution; phase II monitoring; quality control; MEWMA chart.
%! The Monitoring of Dirichlet Compositional Data
%K Compositional data; Dirichlet distribution; phase II monitoring; quality control; MEWMA chart.
%X 

Compositional data are used in many applications such as Cement, Asphalt, and many other Chemical industries. Such data represent random variables whose values must sum up to a certain constant. Quality engineers and technicians require monitoring compositional data and detecting the source of the irregularity in the process as soon as it happens. Throughout the literature, complicated methods were introduced to monitor compositional data. Such methods are computationally complex and can lead to difficulties in interpreting the results. The Dirichlet distribution is commonly used in the literature to model compositional data. In this study, we propose three simple methods to monitor the mean vector of the Dirichlet distribution. The first method is based on a MEWMA control chart. The second method is based on transforming the Dirichlet random variables into beta random variables and then monitoring them using multiple EWMA control charts, while the third method uses multiple EWMA control charts for transformed independent random variables. Using a simulation technique, the performance of the three methods is investigated, and the three methods performed very well under different sample sizes, many random variables, and values of the distribution parameters. When the process is out-of-control, the source of the out-of-control signal can be detected using Method 2 and Method 3. Method 2 maintained its good performance with a probability 0.99 of correctly detecting the source of the signal. Method 3 performed well except for the case of Dirichlet parameter values less than one. However, it maintained almost a probability of correct detection of at least 90% in most cases. The three proposed methods are simple, do not need complicated calculations, and can easily be applied and used by practitioners.

%U http://ijaseit.insightsociety.org/index.php?option=com_content&view=article&id=9&Itemid=1&article_id=13429 %R doi:10.18517/ijaseit.11.5.13429 %J International Journal on Advanced Science, Engineering and Information Technology %V 11 %N 5 %@ 2088-5334

IEEE

Reham W. Elshaer and Aya A. Aly,"The Monitoring of Dirichlet Compositional Data," International Journal on Advanced Science, Engineering and Information Technology, vol. 11, no. 5, pp. 1868-1875, 2021. [Online]. Available: http://dx.doi.org/10.18517/ijaseit.11.5.13429.

RefMan/ProCite (RIS)

TY  - JOUR
AU  - Elshaer, Reham W.
AU  - Aly, Aya A.
PY  - 2021
TI  - The Monitoring of Dirichlet Compositional Data
JF  - International Journal on Advanced Science, Engineering and Information Technology; Vol. 11 (2021) No. 5
Y2  - 2021
SP  - 1868
EP  - 1875
SN  - 2088-5334
PB  - INSIGHT - Indonesian Society for Knowledge and Human Development
KW  - Compositional data; Dirichlet distribution; phase II monitoring; quality control; MEWMA chart.
N2  - 

Compositional data are used in many applications such as Cement, Asphalt, and many other Chemical industries. Such data represent random variables whose values must sum up to a certain constant. Quality engineers and technicians require monitoring compositional data and detecting the source of the irregularity in the process as soon as it happens. Throughout the literature, complicated methods were introduced to monitor compositional data. Such methods are computationally complex and can lead to difficulties in interpreting the results. The Dirichlet distribution is commonly used in the literature to model compositional data. In this study, we propose three simple methods to monitor the mean vector of the Dirichlet distribution. The first method is based on a MEWMA control chart. The second method is based on transforming the Dirichlet random variables into beta random variables and then monitoring them using multiple EWMA control charts, while the third method uses multiple EWMA control charts for transformed independent random variables. Using a simulation technique, the performance of the three methods is investigated, and the three methods performed very well under different sample sizes, many random variables, and values of the distribution parameters. When the process is out-of-control, the source of the out-of-control signal can be detected using Method 2 and Method 3. Method 2 maintained its good performance with a probability 0.99 of correctly detecting the source of the signal. Method 3 performed well except for the case of Dirichlet parameter values less than one. However, it maintained almost a probability of correct detection of at least 90% in most cases. The three proposed methods are simple, do not need complicated calculations, and can easily be applied and used by practitioners.

UR - http://ijaseit.insightsociety.org/index.php?option=com_content&view=article&id=9&Itemid=1&article_id=13429 DO - 10.18517/ijaseit.11.5.13429

RefWorks

RT Journal Article
ID 13429
A1 Elshaer, Reham W.
A1 Aly, Aya A.
T1 The Monitoring of Dirichlet Compositional Data
JF International Journal on Advanced Science, Engineering and Information Technology
VO 11
IS 5
YR 2021
SP 1868
OP 1875
SN 2088-5334
PB INSIGHT - Indonesian Society for Knowledge and Human Development
K1 Compositional data; Dirichlet distribution; phase II monitoring; quality control; MEWMA chart.
AB 

Compositional data are used in many applications such as Cement, Asphalt, and many other Chemical industries. Such data represent random variables whose values must sum up to a certain constant. Quality engineers and technicians require monitoring compositional data and detecting the source of the irregularity in the process as soon as it happens. Throughout the literature, complicated methods were introduced to monitor compositional data. Such methods are computationally complex and can lead to difficulties in interpreting the results. The Dirichlet distribution is commonly used in the literature to model compositional data. In this study, we propose three simple methods to monitor the mean vector of the Dirichlet distribution. The first method is based on a MEWMA control chart. The second method is based on transforming the Dirichlet random variables into beta random variables and then monitoring them using multiple EWMA control charts, while the third method uses multiple EWMA control charts for transformed independent random variables. Using a simulation technique, the performance of the three methods is investigated, and the three methods performed very well under different sample sizes, many random variables, and values of the distribution parameters. When the process is out-of-control, the source of the out-of-control signal can be detected using Method 2 and Method 3. Method 2 maintained its good performance with a probability 0.99 of correctly detecting the source of the signal. Method 3 performed well except for the case of Dirichlet parameter values less than one. However, it maintained almost a probability of correct detection of at least 90% in most cases. The three proposed methods are simple, do not need complicated calculations, and can easily be applied and used by practitioners.

LK http://ijaseit.insightsociety.org/index.php?option=com_content&view=article&id=9&Itemid=1&article_id=13429 DO - 10.18517/ijaseit.11.5.13429