RUcore: Rutgers University Community Repository
Statistical modeling and inference for multiple temporal or spatial cluster detection
Descriptive
TypeOfResource
Text
TitleInfo
(ID = T-1)
Title
Statistical modeling and inference for multiple temporal or spatial cluster detection
Identifier
ETD_1166
Identifier
(type = hdl)
http://hdl.rutgers.edu/1782.2/rucore10001600001.ETD.000050446
Language
LanguageTerm
(authority = ISO639-2);
(type = code)
eng
Genre
(authority = marcgt)
theses
Subject
(ID = SBJ-1);
(authority = RUETD)
Topic
Statistics and Biostatistics
Subject
(ID = SBJ-1);
(authority = ETD-LCSH)
Topic
Cluster analysis
Abstract
This thesis develops a latent modeling framework and likelihood based inference tool to detect multiple temporal or spatial clusters.
Cluster detection is important to researchers from various fields. Practical applications include: biological studies of DNA sequencing, environmental researches, epidemiological studies and surveillance for biological terrorism. The traditional scan statistics procedures have technical difficulties to detect multiple clusters of varying sizes. Some Bayesian approaches have to limit
the potential clusters in cell divisions. A recently proposed stepwise regression method tends to be inefficient in some cases. We utilize some probability distributions to model the latent clusters and mimic the sample data generation process. With model selection techniques, we can obtain an optimal number of total potential clusters. Based on a Monte-Carlo EM algorithm and likelihood based inference, we are able to estimate the associated model parameters, detect significant clusters and identify their locations and sizes. Compared with other procedures, this new approach is intuitive and simple. It is also more efficient and flexible for further extensions.
Cluster detection is important to researchers from various fields. Practical applications include: biological studies of DNA sequencing, environmental researches, epidemiological studies and surveillance for biological terrorism. The traditional scan statistics procedures have technical difficulties to detect multiple clusters of varying sizes. Some Bayesian approaches have to limit
the potential clusters in cell divisions. A recently proposed stepwise regression method tends to be inefficient in some cases. We utilize some probability distributions to model the latent clusters and mimic the sample data generation process. With model selection techniques, we can obtain an optimal number of total potential clusters. Based on a Monte-Carlo EM algorithm and likelihood based inference, we are able to estimate the associated model parameters, detect significant clusters and identify their locations and sizes. Compared with other procedures, this new approach is intuitive and simple. It is also more efficient and flexible for further extensions.
PhysicalDescription
Extent
xi, 79 p. : ill.
InternetMediaType
application/pdf
InternetMediaType
text/xml
Note
(type = degree)
Ph.D.
Note
(type = bibliography)
Includes bibliographical references (p. 75-78)
Note
(type = statement of responsibility)
by Qiankun Sun
Name
(ID = NAME-1);
(type = personal)
NamePart
(type = family)
Sun
NamePart
(type = given)
Qiankun
Role
RoleTerm
(authority = RULIB);
(type = )
author
DisplayForm
Qiankun Sun
Name
(ID = NAME-2);
(type = personal)
NamePart
(type = family)
Xie
NamePart
(type = given)
Minge
Role
RoleTerm
(authority = RULIB);
(type = )
chair
Affiliation
Advisory Committee
DisplayForm
Minge Xie
Name
(ID = NAME-3);
(type = personal)
NamePart
(type = family)
Naus
NamePart
(type = given)
Joseph
Role
RoleTerm
(authority = RULIB);
(type = )
co-chair
Affiliation
Advisory Committee
DisplayForm
Joseph Naus
Name
(ID = NAME-4);
(type = personal)
NamePart
(type = family)
Kolassa
NamePart
(type = given)
John
Role
RoleTerm
(authority = RULIB);
(type = )
internal member
Affiliation
Advisory Committee
DisplayForm
John Kolassa
Name
(ID = NAME-5);
(type = personal)
NamePart
(type = family)
Zhao
NamePart
(type = given)
Jun
Role
RoleTerm
(authority = RULIB);
(type = )
outside member
Affiliation
Advisory Committee
DisplayForm
Jun Zhao
Name
(ID = NAME-1);
(type = corporate)
NamePart
Rutgers University
Role
RoleTerm
(authority = RULIB);
(type = )
degree grantor
Name
(ID = NAME-2);
(type = corporate)
NamePart
Graduate School - New Brunswick
Role
RoleTerm
(authority = RULIB);
(type = )
school
OriginInfo
DateCreated
(point = );
(qualifier = exact)
2008
DateOther
(qualifier = exact);
(type = degree)
2008-10
Place
PlaceTerm
(type = code)
xx
RelatedItem
(type = host)
TitleInfo
Title
Rutgers University Electronic Theses and Dissertations
Identifier
(type = RULIB)
ETD
RelatedItem
(type = host)
TitleInfo
Title
Graduate School - New Brunswick Electronic Theses and Dissertations
Identifier
(type = local)
rucore19991600001
Location
PhysicalLocation
(authority = marcorg);
(displayLabel = Rutgers, The State University of New Jersey)
NjNbRU
Identifier
(type = doi)
doi:10.7282/T38W3DMQ
Genre
(authority = ExL-Esploro)
ETD doctoral
Back to the top
Rights
RightsDeclaration
(AUTHORITY = GS);
(ID = rulibRdec0006)
The author owns the copyright to this work.
Copyright
Status
Copyright protected
Availability
Status
Open
RightsEvent
(AUTHORITY = rulib);
(ID = 1)
Type
Permission or license
Detail
Non-exclusive ETD license
AssociatedObject
(AUTHORITY = rulib);
(ID = 1)
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.
Back to the top
Technical
ContentModel
ETD
MimeType
(TYPE = file)
application/pdf
MimeType
(TYPE = container)
application/x-tar
FileSize
(UNIT = bytes)
1146880
Checksum
(METHOD = SHA1)
ece7c5870bcb8d9ba38d4807d1e0971dfac66052
Back to the top
Statistical Profile