Likelihood-Based EWMA Charts for Monitoring Poisson Count Data With Time-Varying Sample Sizes

DOI10.1080/01621459.2012.682811zbMath1395.62378OpenAlexW2148384753MaRDI QIDQ4648550

Qin Zhou, Zhaojun Wang, Wei Jiang, Changliang Zou

Publication date: 9 November 2012

Published in: Journal of the American Statistical Association (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1080/01621459.2012.682811

zbMATH Keywords

statistical process controlrun length distributionaverage run lengthEWMAhealthcare, Poisson count datashort-run processes

Mathematics Subject Classification ID

Time series, auto-correlation, regression, etc. in statistics (GARCH) (62M10) Applications of statistics to biology and medical sciences; meta analysis (62P10) Applications of statistics in engineering and industry; control charts (62P30)

Related Items (7)

An economic statistical design of the Poisson EWMA control charts for monitoring nonconformities ⋮ A Phase II control chart based on the weighted likelihood ratio test for monitoring polynomial profiles ⋮ A distribution-free multivariate CUSUM control chart using dynamic control limits ⋮ An ARL-unbiased thinning-based EWMA chart to monitor counts ⋮ Online monitoring of the Weibull distributed process based on progressive type II censoring scheme ⋮ Unnamed Item ⋮ A weighted likelihood ratio test-based chart for monitoring process mean and variability

Cites Work

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• Early detection of a change in Poisson rate after accounting for population size effects
• Optimal Surveillance Based on Exponentially Weighted Moving Averages
• Monitoring Poisson Observations Using Modified Exponentially Weighted Moving Average Control Charts
• Is Average Run Length to False Alarm Always an Informative Criterion?
• Evaluations of some Exponentially Weighted Moving Average methods
• Surveillance Strategies for Detecting Changepoint in Incidence Rate Based on Exponentially Weighted Moving Average Methods
• A Reference-Free Cuscore Chart for Dynamic Mean Change Detection and a Unified Framework for Charting Performance Comparison

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