Monitoring and Change Point Estimation of AR(1) Autocorrelated Polynomial Profiles

Authors

  • Mehdi Keramatpour Industrial & Mechanical Engineering, Islamic Azad University, Qazvin Branch
  • S.T.A. Niaki Industrial Engineering, Sharif University of Technology
Abstract:

In this paper, a remedial measure is first proposed to eliminate the effect of autocorrelation in phase-ІІ monitoring of autocorrelated polynomial profiles, where there is a first order autoregressive (AR(1)) relation between the error terms in each profile. Then, a control chart based on the generalized linear test (GLT) is proposed to monitor the coefficients of polynomial profiles and an R-chart is used to monitor the error variance, the combination of which is called GLT/R chart. The performance of the proposed GLT/R chart is evaluated by comparing it to ones of prevalent methods including multivariate T2, EWMA/R and T2 residual control charts, in terms of the average run length (ARL) criterion. Furthermore, an estimator based on the likelihood ratio approach is proposed to estimate the change point in the parameters of autocorrelated polynomial profiles. The results of extensive simulation experiments show good performances of the proposed estimator.

Upgrade to premium to download articles

Sign up to access the full text

Already have an account?login

similar resources

Phase-II Monitoring of AR (1) Autocorrelated Polynomial Profiles

In some statistical process control applications, quality of a process or product can be characterized by a relationship between a response and one or more independent variables, which is typically referred to a profile. In this paper, polynomial profiles are considered to monitor processes in which there is a first order autoregressive relation between the error terms in each profile. A remedi...

full text

Isotonic Change Point Estimation in the AR(1) Autocorrelated Simple Linear Profiles

Sometimes the relationship between dependent and explanatory variable(s) known as profile is monitored. Simple linear profiles among the other types of profiles have been more considered due to their applications especially in calibration. There are some studies on the monitoring them when the observations within each profile are autocorrelated. On the other hand, estimating the change point le...

full text

phase-ii monitoring of ar (1) autocorrelated polynomial profiles

in some statistical process control applications, quality of a process or product can be characterized by a relationship between a response and one or more independent variables, which is typically referred to a profile. in this paper, polynomial profiles are considered to monitor processes in which there is a first order autoregressive relation between the error terms in each profile. a remedi...

full text

change point estimation in the mean of polynomial profiles under drift

in this paper, drift change point estimation in the mean of polynomial profiles is considered. for this purpose, the proposed change point estimator is computed using maximum likelihood approach. performance of the proposed estimator is evaluated using monte carlo simulations when t2 control chart issues an out-of-control signal. simulation results show that the performance of the proposed esti...

full text

Change Point Estimation in Monitoring Survival Time

Precise identification of the time when a change in a hospital outcome has occurred enables clinical experts to search for a potential special cause more effectively. In this paper, we develop change point estimation methods for survival time of a clinical procedure in the presence of patient mix in a Bayesian framework. We apply Bayesian hierarchical models to formulate the change point where ...

full text

EFFECT OF LOCATION OF EXPLANATORY VARIABLE ON MONITORING POLYNOMIAL QUALITY PROFILES

The quality is typically modeled as the univariate or multivariate distribution of quality characteristic/s. In recent applications of statistical process control, quality profiles in which the relationship between a response and explanatory variable/s is captured and monitored are increasingly used to model the quality. Several techniques have been developed to enhance the speed of detecting c...

full text

My Resources

Save resource for easier access later

Save to my library Already added to my library

{@ msg_add @}


Journal title

volume 26  issue 9

pages  933- 942

publication date 2013-09-01

By following a journal you will be notified via email when a new issue of this journal is published.

Hosted on Doprax cloud platform doprax.com

copyright © 2015-2023