Diagnosis Aids in Multivariate Multiple Linear Regression Profiles Monitoring
- 13 June 2014
- journal article
- research article
- Published by Informa UK Limited in Communications in Statistics - Theory and Methods
- Vol. 43 (14), 3057-3079
- https://doi.org/10.1080/03610926.2012.694543
Abstract
Diagnosis aids in addition to detecting the out-of-control state is an important issue in multivariate multiple linear regression profiles monitoring; because a large number of parameters and profiles in this structure are involved. In this paper, we specifically concentrate on identification of profile(s) and parameter(s) which have changed during the process in multivariate multiple linear regression profiles structure in Phase II. We demonstrate the effectiveness of our proposed approaches through Monte Carlo simulations and a real case study in terms of accuracy percent.Keywords
This publication has 34 references indexed in Scilit:
- Phase II monitoring of multivariate multiple linear regression profilesQuality and Reliability Engineering International, 2011
- A case study on monitoring polynomial profiles in the automotive industryQuality and Reliability Engineering International, 2010
- Phase I Analysis of Multiple Linear Regression ProfilesCommunications in Statistics - Simulation and Computation, 2008
- Monitoring polynomial profiles in quality control applicationsThe International Journal of Advanced Manufacturing Technology, 2008
- Phase I Monitoring of Polynomial ProfilesCommunications in Statistics - Theory and Methods, 2008
- Editorial BoardComputational Statistics & Data Analysis, 2007
- Performance evaluation of two methods for online monitoring of linear calibration profilesInternational Journal of Production Research, 2006
- Investigation and characterization of a control scheme for multivariate quality controlQuality and Reliability Engineering International, 1992
- A Use's Guide to Principal ComponentsWiley Series in Probability and Statistics, 1991
- Identification of out of control quality characteristics in a multivariate manufacturing environmentCommunications in Statistics - Theory and Methods, 1991