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Bayesian methods for non-standard missing data problems

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TypeOfResource
Text
TitleInfo (ID = T-1)
Title
Bayesian methods for non-standard missing data problems
Identifier
ETD_2645
Identifier (type = hdl)
http://hdl.rutgers.edu/1782.2/rucore10001600001.ETD.000052996
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-2); (authority = ETD-LCSH)
Topic
Bayesian statistical decision theory
Subject (ID = SBJ-3); (authority = ETD-LCSH)
Topic
Missing observations (Statistics)--Mathematical models
Abstract (type = abstract)
Missing data presents challenges to statistical analysis in many applications such as clinical trials, cluster detection, etc. This thesis analyzes and develops methodologies in some non-standard missing data problems. We first consider non-ignorable drop-out in longitudinal clinical trials. Common simple approaches such as complete case analysis or last observation carried forward can lead to biased estimates and underestimation of uncertainty. We pursue a model-based approach in the context of Bayesian framework to provide more useful inferences. Second, non-compliance is another way to deviate from pre-designed protocols. Traditional methods circumvent the issue with simplifying assumptions such as intention to treat. Consequently they might produce misleading results. We adopt a counter-factual approach, known as the Rubin Causal Model, essentially reducing the analysis to a missing data problem. We address the issue in particular when drop-out is also involved. In relation to the first two research topics to provide better and more accurate assessment of a treatment or procedure, we develop a Bayesian sequential meta-analysis framework to aggregate results from all available studies. We conduct a case study and build a risk profile of a treatment to provide early alert of emerging problems. Last, the question whether a spatial pattern is randomly distributed has been of interest in many applications. We extend and generalize a latent model approach to overlapping cluster detection. We employ this methodology to design an urban mobile sensor network for the surveillance of nuclear materials. With simulation studies, we demonstrate that the method is efficient and powerful in detection of overlapping clusters.
PhysicalDescription
Form (authority = gmd)
electronic resource
Extent
xi, 107 p. : ill.
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application/pdf
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Note (type = degree)
Ph.D.
Note
Includes abstract
Note
Vita
Note (type = bibliography)
Includes bibliographical references
Note (type = statement of responsibility)
by Jerry Q. Cheng
Name (ID = NAME-1); (type = personal)
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Cheng
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Jerry Q.
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1965-
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Jerry Cheng
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Madigan
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David
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chair
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Advisory Committee
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David Madigan
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Xie
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Minge
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co-chair
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Advisory Committee
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Minge Xie
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Hoover
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Donald
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internal member
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Advisory Committee
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Donald Hoover
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NamePart (type = family)
Carpenter
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Tamra
Role
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outside member
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Advisory Committee
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Tamra Carpenter
Name (ID = NAME-1); (type = corporate)
NamePart
Rutgers University
Role
RoleTerm (authority = RULIB)
degree grantor
Name (ID = NAME-2); (type = corporate)
NamePart
Graduate School - New Brunswick
Role
RoleTerm (authority = RULIB)
school
OriginInfo
DateCreated (qualifier = exact)
2010
DateOther (qualifier = exact); (type = degree)
2010
Place
PlaceTerm (type = code)
xx
Location
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NjNbRU
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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
Identifier (type = doi)
doi:10.7282/T31836JZ
Genre (authority = ExL-Esploro)
ETD doctoral
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Rights

RightsDeclaration (AUTHORITY = GS); (ID = rulibRdec0006)
The author owns the copyright to this work.
Copyright
Status
Copyright protected
Availability
Status
Open
Reason
Permission or license
RightsHolder (ID = PRH-1); (type = personal)
Name
FamilyName
Cheng
GivenName
Jerry
Role
Copyright Holder
RightsEvent (AUTHORITY = rulib); (ID = RE-1)
Type
Permission or license
DateTime
2010-04-15 20:13:51
AssociatedEntity (AUTHORITY = rulib); (ID = AE-1)
Role
Copyright holder
Name
Jerry Cheng
Affiliation
Rutgers University. Graduate School - New Brunswick
AssociatedObject (AUTHORITY = rulib); (ID = AO-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.
RightsEvent (AUTHORITY = rulib); (ID = RE-2)
Type
Embargo
DateTime
2010-04-15
Detail
365 days
AssociatedEntity (AUTHORITY = rulib); (ID = AE-1)
Role
Copyright holder
Name
Jerry Cheng
Affiliation
Rutgers University. Graduate School - New Brunswick
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Technical

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ETD
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application/pdf
MimeType (TYPE = container)
application/x-tar
FileSize (UNIT = bytes)
829440
Checksum (METHOD = SHA1)
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