Phase II monitoring of binary profiles in the presence of within-profile autocorrelation based on Markov Model

Mohammad Reza Maleki · Amirhossein Amiri · Ali Reza Taheriyoun

23 citations0/0 files storedCommunications in Statistics - Simulation and Computation

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Advanced Statistical Process Monitoring Fault Detection and Control Systems Optimal Experimental Design Methods

Abstract

This paper introduces a Markov model in Phase II profile monitoring with autocorrelated binary response variable. In the proposed approach, a logistic regression model is extended to describe the within-profile autocorrelation. The likelihood function is constructed and then a particle swarm optimization algorithm (PSO) is tuned and utilized to estimate the model parameters. Furthermore, two control charts are extended in which the covariance matrix is derived based on the Fisher information matrix. Simulation studies are conducted to evaluate the detecting capability of the proposed control charts. A numerical example is also given to illustrate the application of the proposed method.

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References

1981Multiple Attribute Decision Making: Methods and Applications 1992A Multivariate Exponentially Weighted Moving Average Control Chart 2003On the Monitoring of Linear Profiles 2007Current research on profile monitoring 2007Monitoring General Linear Profiles Using Multivariate Exponentially Weighted Moving Average Schemes 2006A control chart based on a change-point model for monitoring linear profiles 2008Monitoring Correlation Within Linear Profiles Using Mixed Models 2008Control chart based on likelihood ratio for monitoring linear profiles

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2017Phase I monitoring and change point estimation of autocorrelated poisson regression profiles 2023Monitoring autocorrelated compositional data vectors using an enhanced residuals Hotelling T 2 control chart 2023Monitoring simple linear profiles in the presence of within‐ and between‐profile autocorrelation 2020GLM profile monitoring using robust estimators

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