Monitoring Multinomial Logit Profiles via Log-Linear Models (Quality Engineering Conference Paper)

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...

Full description

Saved in:
Bibliographic Details
Published in:International journal of industrial engineering & production research 2013-06, Vol.24 (2), p.137-142
Main Authors: Rassoul Noorossana, Abbas Saghaei, Hamidreza Izadbakhsh, Omid Aghababaei
Format: Article
Language:English
Subjects:
Average Run Length (ARL)
Loglinear Models
Multinomial Logit Regression
Multivariate Exponentially Weighted Moving Average (MEWMA)
Profile Monitoring
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
cited_by
cites
container_end_page 142
container_issue 2
container_start_page 137
container_title International journal of industrial engineering & production research
container_volume 24
creator Rassoul Noorossana
Abbas Saghaei
Hamidreza Izadbakhsh
Omid Aghababaei
description 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.
format article
fullrecord <record><control><sourceid>doaj</sourceid><recordid>TN_cdi_doaj_primary_oai_doaj_org_article_497fbeb4fb2046f3abd95b7f45bdbb9c</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><doaj_id>oai_doaj_org_article_497fbeb4fb2046f3abd95b7f45bdbb9c</doaj_id><sourcerecordid>oai_doaj_org_article_497fbeb4fb2046f3abd95b7f45bdbb9c</sourcerecordid><originalsourceid>FETCH-doaj_primary_oai_doaj_org_article_497fbeb4fb2046f3abd95b7f45bdbb9c3</originalsourceid><addsrcrecordid>eNqtjMFqwzAQRE1poaHNP-jYHgwmkmL5HFJaiCGFHHoTK3tlNijaICmF_H3d0E_oXGZ4A--uWqyk0rVcy6_7eTeNqZUx3WO1zPnYzGmN1NosKtdzpMKJ4iT6SygU-UQQxI4nKmKf2FPALL4JflG9o4iQRM8jhixePi8QqFzFNk7zgTfLhqPHhHFAsYczptfn6sFDyLj866fq42172LzXI8PRnhOdIF0tA9kb4DRZSIWGgFZ1rXfolHerRq29BDd22rVeaTc61w3yP10_dcNjaA</addsrcrecordid><sourcetype>Open Website</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype></control><display><type>article</type><title>Monitoring Multinomial Logit Profiles via Log-Linear Models (Quality Engineering Conference Paper)</title><source>EZB Electronic Journals Library</source><creator>Rassoul Noorossana ; Abbas Saghaei ; Hamidreza Izadbakhsh ; Omid Aghababaei</creator><creatorcontrib>Rassoul Noorossana ; Abbas Saghaei ; Hamidreza Izadbakhsh ; Omid Aghababaei</creatorcontrib><description>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.</description><identifier>ISSN: 2008-4889</identifier><identifier>EISSN: 2345-363X</identifier><language>eng</language><publisher>Iran University of Science &amp; Technology</publisher><subject>Average Run Length (ARL) ; Loglinear Models ; Multinomial Logit Regression ; Multivariate Exponentially Weighted Moving Average (MEWMA) ; Profile Monitoring</subject><ispartof>International journal of industrial engineering &amp; production research, 2013-06, Vol.24 (2), p.137-142</ispartof><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>314,776,780</link.rule.ids></links><search><creatorcontrib>Rassoul Noorossana</creatorcontrib><creatorcontrib>Abbas Saghaei</creatorcontrib><creatorcontrib>Hamidreza Izadbakhsh</creatorcontrib><creatorcontrib>Omid Aghababaei</creatorcontrib><title>Monitoring Multinomial Logit Profiles via Log-Linear Models (Quality Engineering Conference Paper)</title><title>International journal of industrial engineering &amp; production research</title><description>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.</description><subject>Average Run Length (ARL)</subject><subject>Loglinear Models</subject><subject>Multinomial Logit Regression</subject><subject>Multivariate Exponentially Weighted Moving Average (MEWMA)</subject><subject>Profile Monitoring</subject><issn>2008-4889</issn><issn>2345-363X</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2013</creationdate><recordtype>article</recordtype><sourceid>DOA</sourceid><recordid>eNqtjMFqwzAQRE1poaHNP-jYHgwmkmL5HFJaiCGFHHoTK3tlNijaICmF_H3d0E_oXGZ4A--uWqyk0rVcy6_7eTeNqZUx3WO1zPnYzGmN1NosKtdzpMKJ4iT6SygU-UQQxI4nKmKf2FPALL4JflG9o4iQRM8jhixePi8QqFzFNk7zgTfLhqPHhHFAsYczptfn6sFDyLj866fq42172LzXI8PRnhOdIF0tA9kb4DRZSIWGgFZ1rXfolHerRq29BDd22rVeaTc61w3yP10_dcNjaA</recordid><startdate>20130601</startdate><enddate>20130601</enddate><creator>Rassoul Noorossana</creator><creator>Abbas Saghaei</creator><creator>Hamidreza Izadbakhsh</creator><creator>Omid Aghababaei</creator><general>Iran University of Science &amp; Technology</general><scope>DOA</scope></search><sort><creationdate>20130601</creationdate><title>Monitoring Multinomial Logit Profiles via Log-Linear Models (Quality Engineering Conference Paper)</title><author>Rassoul Noorossana ; Abbas Saghaei ; Hamidreza Izadbakhsh ; Omid Aghababaei</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-doaj_primary_oai_doaj_org_article_497fbeb4fb2046f3abd95b7f45bdbb9c3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2013</creationdate><topic>Average Run Length (ARL)</topic><topic>Loglinear Models</topic><topic>Multinomial Logit Regression</topic><topic>Multivariate Exponentially Weighted Moving Average (MEWMA)</topic><topic>Profile Monitoring</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Rassoul Noorossana</creatorcontrib><creatorcontrib>Abbas Saghaei</creatorcontrib><creatorcontrib>Hamidreza Izadbakhsh</creatorcontrib><creatorcontrib>Omid Aghababaei</creatorcontrib><collection>DOAJ Directory of Open Access Journals</collection><jtitle>International journal of industrial engineering &amp; production research</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Rassoul Noorossana</au><au>Abbas Saghaei</au><au>Hamidreza Izadbakhsh</au><au>Omid Aghababaei</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Monitoring Multinomial Logit Profiles via Log-Linear Models (Quality Engineering Conference Paper)</atitle><jtitle>International journal of industrial engineering &amp; production research</jtitle><date>2013-06-01</date><risdate>2013</risdate><volume>24</volume><issue>2</issue><spage>137</spage><epage>142</epage><pages>137-142</pages><issn>2008-4889</issn><eissn>2345-363X</eissn><abstract>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.</abstract><pub>Iran University of Science &amp; Technology</pub><oa>free_for_read</oa></addata></record>
fulltext fulltext
identifier ISSN: 2008-4889
ispartof International journal of industrial engineering & production research, 2013-06, Vol.24 (2), p.137-142
issn 2008-4889
2345-363X
language eng
recordid cdi_doaj_primary_oai_doaj_org_article_497fbeb4fb2046f3abd95b7f45bdbb9c
source EZB Electronic Journals Library
subjects Average Run Length (ARL)
Loglinear Models
Multinomial Logit Regression
Multivariate Exponentially Weighted Moving Average (MEWMA)
Profile Monitoring
title Monitoring Multinomial Logit Profiles via Log-Linear Models (Quality Engineering Conference Paper)
url http://sfxeu10.hosted.exlibrisgroup.com/loughborough?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-01-22T20%3A56%3A14IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-doaj&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Monitoring%20Multinomial%20Logit%20Profiles%20via%20Log-Linear%20Models%20(Quality%20Engineering%20Conference%20Paper)&rft.jtitle=International%20journal%20of%20industrial%20engineering%20&%20production%20research&rft.au=Rassoul%20Noorossana&rft.date=2013-06-01&rft.volume=24&rft.issue=2&rft.spage=137&rft.epage=142&rft.pages=137-142&rft.issn=2008-4889&rft.eissn=2345-363X&rft_id=info:doi/&rft_dat=%3Cdoaj%3Eoai_doaj_org_article_497fbeb4fb2046f3abd95b7f45bdbb9c%3C/doaj%3E%3Cgrp_id%3Ecdi_FETCH-doaj_primary_oai_doaj_org_article_497fbeb4fb2046f3abd95b7f45bdbb9c3%3C/grp_id%3E%3Coa%3E%3C/oa%3E%3Curl%3E%3C/url%3E&rft_id=info:oai/&rft_id=info:pmid/&rfr_iscdi=true