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Advances in confidence distribution

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TitleInfo
Title
Advances in confidence distribution
SubTitle
individualized fusion learning and predictive distribution function
Name (type = personal)
NamePart (type = family)
Shen
NamePart (type = given)
Jieli
NamePart (type = date)
1990-
DisplayForm
Jieli Shen
Role
RoleTerm (authority = RULIB)
author
Name (type = personal)
NamePart (type = family)
Xie
NamePart (type = given)
Minge
DisplayForm
Minge Xie
Affiliation
Advisory Committee
Role
RoleTerm (authority = RULIB)
chair
Name (type = personal)
NamePart (type = family)
Liu
NamePart (type = given)
Regina Y.
DisplayForm
Regina Y. Liu
Affiliation
Advisory Committee
Role
RoleTerm (authority = RULIB)
internal member
Name (type = personal)
NamePart (type = family)
Wang
NamePart (type = given)
Sijian
DisplayForm
Sijian Wang
Affiliation
Advisory Committee
Role
RoleTerm (authority = RULIB)
internal member
Name (type = personal)
NamePart (type = family)
Lu
NamePart (type = given)
Shou-En
DisplayForm
Shou-En Lu
Affiliation
Advisory Committee
Role
RoleTerm (authority = RULIB)
outside member
Name (type = corporate)
NamePart
Rutgers University
Role
RoleTerm (authority = RULIB)
degree grantor
Name (type = corporate)
NamePart
School of Graduate Studies
Role
RoleTerm (authority = RULIB)
school
TypeOfResource
Text
Genre (authority = marcgt)
theses
OriginInfo
DateCreated (qualifier = exact)
2017
DateOther (qualifier = exact); (type = degree)
2017-10
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2017
Place
PlaceTerm (type = code)
xx
Language
LanguageTerm (authority = ISO639-2b); (type = code)
eng
Abstract (type = abstract)
In this dissertation, we develop new methods for problems for the two fundamental topics of statistical learning - inference and prediction, using the tool of confidence distribution (CD). Specifically, we are interested in i) making efficient and valid statistical inference about an individual subject, by borrowing information from other individual subjects with similar traits, in a heterogeneous database that contains many individual subjects, and ii) effectively and accurately quantifying uncertainties associated with the prediction of future observations from a model estimated based on past observations. For the first problem, we propose an individualized fusion learning (iFusion) approach, for drawing efficient individualized inference by fusing information from relevant data sources. iFusion is robust for handling heterogeneity arising from diverse sources, and is ideally suited for goal-directed applications such as precision medicine. Specifically, iFusion summarizes individual inferences as CDs, then adaptively forms a clique of individuals that bears relevance to the target individual, and finally combines the CDs from those relevant individuals and draws inference for the target individual based on it. In essence, iFusion “borrows strength” from relevant individuals to improve inference efficiency while retaining inference validity. Computationally, it is parallel in nature and scales up well in comparison with its competitors such as many of the Bayesian methods. Examples in simulations and a real application in financial forecasting are further presented to demonstrate the effectiveness of iFusion. For the second problem, a general prediction framework is proposed in which prediction is presented in the form of a predictive distribution function. This predictive distribution function is well suited for the notion of confidence subscribed in the frequentist interpretation, and can provide meaningful answers for questions related to prediction. A general approach under this framework is formulated and illustrated by using the concept of CD. This CD-based prediction approach inherits many desirable properties of CD, including its capacity for serving as a common platform for connecting and unifying the existing procedures of predictive inference in Bayesian, fiducial and frequentist paradigms. The theory underlying the CD-based predictive distribution is developed and some related efficiency and optimality issues are addressed. Moreover, a simple yet broadly applicable Monte-Carlo algorithm is proposed for the implementation of the proposed approach. This concrete algorithm together with the proposed definition and associate theoretical development produces a comprehensive statistical inference framework for prediction. Finally, the approach is applied to simulation studies, and a real project on predicting the incoming volume of application submissions to a government agency. The latter shows the applicability of the proposed approach to dependent data settings.
Subject (authority = RUETD)
Topic
Statistics and Biostatistics
RelatedItem (type = host)
TitleInfo
Title
Rutgers University Electronic Theses and Dissertations
Identifier (type = RULIB)
ETD
Identifier
ETD_8432
PhysicalDescription
Form (authority = gmd)
electronic resource
InternetMediaType
application/pdf
InternetMediaType
text/xml
Extent
1 online resource (x, 86 p. : ill.)
Note (type = degree)
Ph.D.
Note (type = bibliography)
Includes bibliographical references
Subject (authority = ETD-LCSH)
Topic
Confidence intervals
Note (type = statement of responsibility)
by Jieli Shen
RelatedItem (type = host)
TitleInfo
Title
School of Graduate Studies Electronic Theses and Dissertations
Identifier (type = local)
rucore10001600001
Location
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NjNbRU
Identifier (type = doi)
doi:10.7282/T3KS6VQB
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
Shen
GivenName
Jieli
Role
Copyright Holder
RightsEvent
Type
Permission or license
DateTime (encoding = w3cdtf); (qualifier = exact); (point = start)
2017-09-28 01:30:00
AssociatedEntity
Name
Jieli Shen
Role
Copyright holder
Affiliation
Rutgers University. School of Graduate Studies
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)
2017-10-31
DateTime (encoding = w3cdtf); (qualifier = exact); (point = end)
2018-05-02
Type
Embargo
Detail
Access to this PDF has been restricted at the author's request. It will be publicly available after May 2nd, 2018.
Copyright
Status
Copyright protected
Availability
Status
Open
Reason
Permission or license
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Technical

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2017-10-03T15:09:41
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2017-10-03T15:09:41
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