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Change point detection in univariate and multivariate processes

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TitleInfo
Title
Change point detection in univariate and multivariate processes
Name (type = personal)
NamePart (type = family)
Kim
NamePart (type = given)
Jinho
NamePart (type = date)
1972-
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Jinho Kim
Role
RoleTerm (authority = RULIB)
author
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Elsayed
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Elsayed A
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Elsayed A Elsayed
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Advisory Committee
Role
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chair
Name (type = personal)
NamePart (type = family)
Jeong
NamePart (type = given)
Myong K
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Myong K Jeong
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Advisory Committee
Role
RoleTerm (authority = RULIB)
co-chair
Name (type = personal)
NamePart (type = family)
Pham
NamePart (type = given)
Hoang
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Hoang Pham
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Advisory Committee
Role
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internal member
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Xie
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Min-ge
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Min-ge Xie
Affiliation
Advisory Committee
Role
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outside member
Name (type = corporate)
NamePart
Rutgers University
Role
RoleTerm (authority = RULIB)
degree grantor
Name (type = corporate)
NamePart
Graduate School - New Brunswick
Role
RoleTerm (authority = RULIB)
school
TypeOfResource
Text
Genre (authority = marcgt)
theses
OriginInfo
DateCreated (qualifier = exact)
2014
DateOther (qualifier = exact); (type = degree)
2014-10
CopyrightDate (encoding = w3cdtf)
2014
Place
PlaceTerm (type = code)
xx
Language
LanguageTerm (authority = ISO639-2b); (type = code)
eng
Abstract (type = abstract)
In this proposal, we present several methodologies for change point detection in univariate and multivariate processes, identifying fault variables in multivariate processes, and detecting changes in multistage processes. We first propose an adaptive runs rule, which is motivated by the concept of supplementary runs rule, in order to make univariate control charts more sensitive to small mean shifts. The adaptive runs rule assigns scores to consecutive runs based on the estimated shift size of the mean. We supplement the Adaptive CUSUM (ACUSUM) chart with the adaptive runs rule to enhance its sensitivity in detecting small mean shifts. We then present two new multivariate SPC procedures, MASC and AMASC, for detecting general mean shift vectors based on the approximate sequential test, which uses an approximate likelihood ratio of a central and a noncentral distribution. Similar to the univariate CUSUM chart, a multivariate CUSUM chart can be designed to detect a particular size of the mean shift optimally based on the scheme of a sequential likelihood ratio test for noncentrality. However, in multivariate case, the probability ratio of a sequential test is intractable mathematically and the test statistic based on the ratio does not have a closed form expression which makes it impractical for real application. We drive an approximate log-likelihood ratio and propose a multivariate SPC chart based on the sequential test. We propose an adaptive step-down procedure using conditional statistics for the identification of fault variables. In a process with massive process variables (high-dimensional process), identifying which variable or a subset of variables causes an out-of-control signal is a challenging issue for quality engineers. The proposed step-down procedure selects a variable having no significant evidence of a change at each step based on the variables that are selected in previous steps. When the number of fault variables is small, the selected variables are useful to construct powerful conditional test statistics for identifying the shifted components of the mean vector. The proposed procedure yields a reasonable computational complexity in a high-dimensional process, since it is based on polynomial time algorithm. Finally, we model an autocorrelated multistage process as VAR(1) model and derive the propagation models of mean shifts to subsequent stages under the state space model. Further, we propose a new conditional multivariate EWMA (CMEWMA) chart to detect the shift of mean in a multistage process by incorporating unchanged stage information. The simulation results show that the proposed MEWMA chart is efficient in detecting a wide range of small mean shifts compared with the observation-based and residual-based MEWMA charts.
Subject (authority = RUETD)
Topic
Industrial and Systems Engineering
Subject (authority = ETD-LCSH)
Topic
Process control--Statistical methods
Subject (authority = ETD-LCSH)
Topic
Multivariate analysis
RelatedItem (type = host)
TitleInfo
Title
Rutgers University Electronic Theses and Dissertations
Identifier (type = RULIB)
ETD
Identifier
ETD_5746
PhysicalDescription
Form (authority = gmd)
electronic resource
InternetMediaType
application/pdf
InternetMediaType
text/xml
Extent
1 online resource (xi, 115 p. : ill.)
Note (type = degree)
Ph.D.
Note (type = bibliography)
Includes bibliographical references
Note (type = statement of responsibility)
by Jinho Kim
RelatedItem (type = host)
TitleInfo
Title
Graduate School - New Brunswick Electronic Theses and Dissertations
Identifier (type = local)
rucore19991600001
Location
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NjNbRU
Identifier (type = doi)
doi:10.7282/T3HQ3XDP
Genre (authority = ExL-Esploro)
ETD doctoral
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Rights

RightsDeclaration (ID = rulibRdec0006)
The author owns the copyright to this work.
RightsHolder (type = personal)
Name
FamilyName
Kim
GivenName
Jinho
Role
Copyright Holder
RightsEvent
Type
Permission or license
DateTime (encoding = w3cdtf); (qualifier = exact); (point = start)
2014-07-29 12:58:38
AssociatedEntity
Name
JINHO KIM
Role
Copyright holder
Affiliation
Rutgers University. Graduate School - New Brunswick
AssociatedObject
Type
License
Name
Author Agreement License
Detail
I hereby grant to the Rutgers University Libraries and to my school the non-exclusive right to archive, reproduce and distribute my thesis or dissertation, in whole or in part, and/or my abstract, in whole or in part, in and from an electronic format, subject to the release date subsequently stipulated in this submittal form and approved by my school. I represent and stipulate that the thesis or dissertation and its abstract are my original work, that they do not infringe or violate any rights of others, and that I make these grants as the sole owner of the rights to my thesis or dissertation and its abstract. I represent that I have obtained written permissions, when necessary, from the owner(s) of each third party copyrighted matter to be included in my thesis or dissertation and will supply copies of such upon request by my school. I acknowledge that RU ETD and my school will not distribute my thesis or dissertation or its abstract if, in their reasonable judgment, they believe all such rights have not been secured. I acknowledge that I retain ownership rights to the copyright of my work. I also retain the right to use all or part of this thesis or dissertation in future works, such as articles or books.
Copyright
Status
Copyright protected
Availability
Status
Open
Reason
Permission or license
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ETD
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windows xp
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