What is it about?
This research builds four multivariate quality control charts using Bayesian sequential methods and state space models, applying Bayes factors to detect if a process is in control for complex multivariate t and Bessel-distributed data.
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Why is it important?
It advances quality control by providing tools for monitoring processes with heavy-tailed or complex distributions, improving detection accuracy and reliability in manufacturing or other multivariate data contexts.
Perspectives
Future work could apply these charts to real industry data, explore alternative Bayesian methods, and extend the framework to other non-normal multivariate distributions and dynamic control processes.
Dr. Delshad Shaker Ismael Botani
Salahaddin University-Erbil
Read the Original
This page is a summary of: تكوين لوحات MEWMA متعددة المتغيرات باستخدام عوامل بيز*, Zanco Journal of Humanity Sciences, February 2018, Salahaddin University - Erbil,
DOI: 10.21271/zjhs.2017.21.6.7.
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