Volume 24, Issue 2 (IJIEPR 2013)                   IJIEPR 2013, 24(2): 137-142 | Back to browse issues page

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Noorossana R, Saghaei A, Izadbakhsh H, Aghababaei O. Monitoring Multinomial Logit Profiles via Log-Linear Models (Quality Engineering Conference Paper). IJIEPR 2013; 24 (2) :137-142
URL: http://ijiepr.iust.ac.ir/article-1-497-en.html
Monitoring Multinomial Logit Profiles via Log-Linear Models (Quality Engineering Conference Paper)
Rassoul Noorossana 1, Abbas Saghaei2 , Hamidreza Izadbakhsh3 , Omid Aghababaei4
1- Industrial Engineering Department, Iran University of Science and Technology Tehran, Iran, 16846-13114 , rassoul@iust.ac.ir
2- Industrial Engineering Department, Islamic Azad University, Science and Research Branch, Tehran, Iran
3- Industrial Engineering Department, Iran University of Science and Technology Tehran, Iran, 16846-13114
4- Statistics Department, Faculty of Mathematical Sciences, ShahidBeheshti University, Tehran, Iran
Abstract:   (12181 Views)
In certain statistical process control applications, quality of a process or product can be characterized by a function commonly referred to as profile. Some of the potential applications of profile monitoring are cases where quality characteristic of interest is modelled using binary,multinomial or ordinal variables. In this paper, profiles with multinomial response are studied. For this purpose, multinomial logit regression (MLR) is considered as the basis.Then, the MLR is converted to Poisson GLM with log link. Two methods including Multivariate exponentially weighted moving average (MEWMA) statistics, and Likelihood ratio test (LRT) statistics are proposed to monitor MLR profiles in phase II. Performances of these three methods are evaluated by average run length criterion (ARL). A case study from alloy fasteners manufacturing process is used to illustrate the implementation of the proposed approach. Results indicate satisfactory performance for the proposed method.
Keywords: Loglinear Models, Average Run Length (ARL), Multivariate Exponentially Weighted Moving Average (MEWMA), Multinomial Logit Regression, Profile Monitoring.
Full-Text [PDF 406 kb]   (3127 Downloads)    
Type of Study: Research | Subject: Statistical Process Control Statistical Process Control or Quality Control
Received: 2013/01/28 | Accepted: 2013/06/17 | Published: 2013/06/17
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