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Fusion Learning of dependent studies by confidence distribution (CD)

Descriptive

TitleInfo
Title
Fusion Learning of dependent studies by confidence distribution (CD)
SubTitle
theory and applications
Name (type = personal)
NamePart (type = family)
Li
NamePart (type = given)
Chengrui
NamePart (type = date)
1989-
DisplayForm
Chengrui Li
Role
RoleTerm (authority = RULIB)
author
Name (type = personal)
NamePart (type = family)
Xie
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Minge
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Minge Xie
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Advisory Committee
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chair
Name (type = personal)
NamePart (type = family)
Liu
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Regina
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Regina Liu
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Advisory Committee
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internal member
Name (type = personal)
NamePart (type = family)
Hung
NamePart (type = given)
Ying
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Ying Hung
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Advisory Committee
Role
RoleTerm (authority = RULIB)
internal member
Name (type = personal)
NamePart (type = family)
Cheng
NamePart (type = given)
Jerry
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Jerry Cheng
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)
2017
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2017-05
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2017
Place
PlaceTerm (type = code)
xx
Language
LanguageTerm (authority = ISO639-2b); (type = code)
eng
Abstract (type = abstract)
This dissertation focuses on developing efficient Fusion Learning methodologies for combining information from non-independent sources using {it confidence distribution} (CD). The sources hereby are broadly construed as different pieces of information extracted from possibly correlated datasets. This situation typically arises when multiple inferences are performed over different times, locations or experiment settings due to computational and statistical considerations, which encompasses a wide range of scientific and engineering applications (e.g. seismic monitoring and detection, computer experiments). In this dissertation, we develop a general framework to effectively and efficiently combine these correlated information using CD, and furthermore, explore the advantages of this framework in different problems that are of theoretical and practical interests.
Subject (authority = RUETD)
Topic
Statistics and Biostatistics
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Title
Rutgers University Electronic Theses and Dissertations
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ETD_8005
PhysicalDescription
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electronic resource
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Extent
1 online resource (x, 75 p. : ill.)
Note (type = degree)
Ph.D.
Note (type = bibliography)
Includes bibliographical references
Note (type = statement of responsibility)
by Chengrui Li
RelatedItem (type = host)
TitleInfo
Title
Graduate School - New Brunswick Electronic Theses and Dissertations
Identifier (type = local)
rucore19991600001
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NjNbRU
Identifier (type = doi)
doi:10.7282/T35M68K2
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
Li
GivenName
Chengrui
Role
Copyright Holder
RightsEvent
Type
Permission or license
DateTime (encoding = w3cdtf); (qualifier = exact); (point = start)
2017-04-13 09:09:56
AssociatedEntity
Name
Chengrui Li
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.
RightsEvent
DateTime (encoding = w3cdtf); (qualifier = exact); (point = start)
2017-05-31
DateTime (encoding = w3cdtf); (qualifier = exact); (point = end)
2019-05-31
Type
Embargo
Detail
Access to this PDF has been restricted at the author's request. It will be publicly available after May 31st, 2019.
Copyright
Status
Copyright protected
Availability
Status
Open
Reason
Permission or license
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Technical

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2017-04-19T23:12:56
DateCreated (point = end); (encoding = w3cdtf); (qualifier = exact)
2017-04-19T23:12:56
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