Residual-based cumulative sum charts to monitor time series of counts via copula-based Markov models

Mohammed Alqawba; Jong Min Kim; Taha Radwan

doi:10.1002/asmb.2703

Residual-based cumulative sum charts to monitor time series of counts via copula-based Markov models

Mohammed Alqawba, Jong Min Kim, Taha Radwan

Statistics (Morris)

Research output: Contribution to journal › Article › peer-review

1 Scopus citations

Abstract

Several scientific observations produce data that consist of serially dependent counts that are difficult to accurately analyze due to the absence of normality and the limited literature on dealing with such data. In this article, we propose a cumulative sum chart to monitor serially dependent counts using copula-based Markov models. After reviewing such models, we introduce the randomized quantile residuals obtained from the Markov process. The proposed method is evaluated using a comprehensive simulation study and a real-life example. Results suggested that the method is effective and easily implemented.

Original language	English (US)
Pages (from-to)	1039-1048
Number of pages	10
Journal	Applied Stochastic Models in Business and Industry
Volume	38
Issue number	6
DOIs	https://doi.org/10.1002/asmb.2703
State	Published - Nov 1 2022

Bibliographical note

Funding Information:
The authors thank the editor, the anonymous AE, and referee for the constructive comments, which led to considerable improvement of the manuscript.

Publisher Copyright:
© 2022 John Wiley & Sons, Ltd.

Keywords

Markov chains
copula
count time series
poisson
serial dependence
statistical process control

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10.1002/asmb.2703

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title = "Residual-based cumulative sum charts to monitor time series of counts via copula-based Markov models",

abstract = "Several scientific observations produce data that consist of serially dependent counts that are difficult to accurately analyze due to the absence of normality and the limited literature on dealing with such data. In this article, we propose a cumulative sum chart to monitor serially dependent counts using copula-based Markov models. After reviewing such models, we introduce the randomized quantile residuals obtained from the Markov process. The proposed method is evaluated using a comprehensive simulation study and a real-life example. Results suggested that the method is effective and easily implemented.",

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AU - Alqawba, Mohammed

AU - Kim, Jong Min

AU - Radwan, Taha

N1 - Funding Information: The authors thank the editor, the anonymous AE, and referee for the constructive comments, which led to considerable improvement of the manuscript. Publisher Copyright: © 2022 John Wiley & Sons, Ltd.

PY - 2022/11/1

Y1 - 2022/11/1

N2 - Several scientific observations produce data that consist of serially dependent counts that are difficult to accurately analyze due to the absence of normality and the limited literature on dealing with such data. In this article, we propose a cumulative sum chart to monitor serially dependent counts using copula-based Markov models. After reviewing such models, we introduce the randomized quantile residuals obtained from the Markov process. The proposed method is evaluated using a comprehensive simulation study and a real-life example. Results suggested that the method is effective and easily implemented.

AB - Several scientific observations produce data that consist of serially dependent counts that are difficult to accurately analyze due to the absence of normality and the limited literature on dealing with such data. In this article, we propose a cumulative sum chart to monitor serially dependent counts using copula-based Markov models. After reviewing such models, we introduce the randomized quantile residuals obtained from the Markov process. The proposed method is evaluated using a comprehensive simulation study and a real-life example. Results suggested that the method is effective and easily implemented.

KW - Markov chains

KW - copula

KW - count time series

KW - poisson

KW - serial dependence

KW - statistical process control

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JO - Applied Stochastic Models in Business and Industry

JF - Applied Stochastic Models in Business and Industry

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ER -

Residual-based cumulative sum charts to monitor time series of counts via copula-based Markov models

Abstract

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Keywords

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