Estimating the time of a step change in the multivariate-attribute process mean using ANN and MLE
by Amirhossein Amiri; Mohammad Reza Maleki; Fatemeh Sogandi
International Journal of Data Analysis Techniques and Strategies (IJDATS), Vol. 10, No. 1, 2018

Abstract: In this paper, we consider correlated multivariate-attribute quality characteristics and provide two methods including a modular method based on artificial neural network (ANN) as well as maximum likelihood estimation (MLE) method to estimate the time of change in the parameters of the process mean. We evaluate the performance of the estimators in terms of some criteria in change point estimation and compare them through simulation studies. The results show that the proposed ANN-based model outperforms the MLE approach under most step shifts in the mean vector of the multivariate-attribute process.

Online publication date: Sun, 25-Mar-2018

The full text of this article is only available to individual subscribers or to users at subscribing institutions.

 
Existing subscribers:
Go to Inderscience Online Journals to access the Full Text of this article.

Pay per view:
If you are not a subscriber and you just want to read the full contents of this article, buy online access here.

Complimentary Subscribers, Editors or Members of the Editorial Board of the International Journal of Data Analysis Techniques and Strategies (IJDATS):
Login with your Inderscience username and password:

Username:        Password:          Forgotten your password?

Want to subscribe?
A subscription gives you complete access to all articles in the current issue, as well as to all articles in the previous three years (where applicable). See our Orders page to subscribe.

If you still need assistance, please email subs@inderscience.com