Robust estimation of complicated profiles using wavelets. Issue 4 (16th February 2017)

• Record Type: Journal Article
• Authors: Shahriari, Hamid; Ahmadi, Orod
• Is Part Of: Communications in statistics. Volume 46:Issue 4(2017)
• Page Start: 1573
• Page End: 1593
• Publication Date: 2017-02-16
• Subjects: Phase I -- Profile monitoring -- Robust clustering -- Robust estimator -- S-estimator -- Wavelet transformation; 62F35; Mathematical statistics -- Periodicals; Mathematics; Statistics; 519.2
• DOI: 10.1080/03610926.2015.1024859
• Languages: English
• ISSNs: 0361-0926

Abstract

ABSTRACT: Some quality characteristics are well defined when treated as the response variables and their relationships are identified to some independent variables. This relationship is called a profile. The parametric models, such as linear models, may be used to model the profiles. However, due to the complexity of many processes in practical applications, it is inappropriate to model the process using parametric models. In these cases non parametric methods are used to model the processes. One of the most applicable non parametric methods used to model complicated profiles is the wavelet. Many authors considered the use of the wavelet transformation only for monitoring the processes in phase II. The problem of estimating the in-control profile in phase I using wavelet transformation is not deeply addressed. Usually classical estimators are used in phase I to estimate the in-control profiles, even when the wavelet transformation is used. These estimators are suitable if the data do not contain outliers. However, when the outliers exist, these estimators cannot estimate the in-control profile properly. In this research, a robust method of estimating the in-control profiles is proposed, which is insensitive to the presence of outliers and could be applied when the wavelet transformation is used. The proposed estimator is the combination of the robust clustering and the S-estimator. This estimator is compared with the classical estimator of the in-control profile in the presence of outliers. The results from a large simulation study show that using the proposed method, one can estimate the in-control profile precisely when the data are contaminated either locally or globally.