CUSUM Control Chart for Monitoring Mean of the INAR(1)PCJ Process

A novel lifetime distribution is presented, derived from compounding the Poisson and two-parameter Chris-Jerry distributions. Various of its statistical characteristics are determined, encompassing aspects such as the moments, probability generating function, and hazard rate function. Statistical inference regarding the model parameters is explored through maximum likelihood estimation. A simulation study is carried out to evaluate the bias and mean square error of the estimated values. The importance of the proposed distribution is illustrated within the framework of an integer-valued first-order autoregressive process, which we shall call the INAR(1) process. Additionally, a simulation study is conducted employing the conditional maximum likelihood and conditional least squares methods. The practical significance of the proposed model is validated through its application to real datasets. In the realm of statistical quality control, we devise a cumulative sum control chart for monitoring the process mean. To illustrate its applicability, we conduct both simulation and real-data analysis.

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Publication Details

Journal
Model Assisted Statistics and Applications
Published
2025-02-01
DOI
https://doi.org/10.1177/15741699251318117
Primary Topic
Advanced Statistical Process Monitoring
Type
article
Field-Weighted Citation Impact
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article

CUSUM Control Chart for Monitoring Mean of the INAR(1)PCJ Process

M. R. Irshad, Christophe Chesneau, S Aswathy, R. Maya
Model Assisted Statistics and Applications
Advanced Statistical Process Monitoring
article

CUSUM Control Chart for Monitoring Mean of the INAR(1)PCJ Process

M. R. Irshad, Christophe Chesneau, S Aswathy, R. Maya
article en

Abstract

A novel lifetime distribution is presented, derived from compounding the Poisson and two-parameter Chris-Jerry distributions. Various of its statistical characteristics are determined, encompassing aspects such as the moments, probability generating function, and hazard rate function. Statistical inference regarding the model parameters is explored through maximum likelihood estimation. A simulation study is carried out to evaluate the bias and mean square error of the estimated values. The importance of the proposed distribution is illustrated within the framework of an integer-valued first-order autoregressive process, which we shall call the INAR(1) process. Additionally, a simulation study is conducted employing the conditional maximum likelihood and conditional least squares methods. The practical significance of the proposed model is validated through its application to real datasets. In the realm of statistical quality control, we devise a cumulative sum control chart for monitoring the process mean. To illustrate its applicability, we conduct both simulation and real-data analysis.

Model Assisted Statistics and ApplicationsVol. 20(1)
Cochin University of Science and Technology (IN), Université de Caen Normandie (FR)
Openalex Percentile: Top 96%
Advanced Statistical Process Monitoring
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