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Recent advances in statistical models

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TitleInfo
Title
Recent advances in statistical models
SubTitle
topics in model selection and semi-parametric inference
Name (type = personal)
NamePart (type = family)
Qiao
NamePart (type = given)
Wenqian
NamePart (type = date)
1984-
DisplayForm
Wenqian Qiao
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)
Kolassa
NamePart (type = given)
John
DisplayForm
John Kolassa
Affiliation
Advisory Committee
Role
RoleTerm (authority = RULIB)
internal member
Name (type = personal)
NamePart (type = family)
Rong
NamePart (type = given)
Chen
DisplayForm
Chen Rong
Affiliation
Advisory Committee
Role
RoleTerm (authority = RULIB)
internal member
Name (type = personal)
NamePart (type = family)
Xu
NamePart (type = given)
Li-An
DisplayForm
Li-An Xu
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
Graduate School - New Brunswick
Role
RoleTerm (authority = RULIB)
school
TypeOfResource
Text
Genre (authority = marcgt)
theses
OriginInfo
DateCreated (qualifier = exact)
2012
DateOther (qualifier = exact); (type = degree)
2012-10
Place
PlaceTerm (type = code)
xx
Language
LanguageTerm (authority = ISO639-2b); (type = code)
eng
Abstract (type = abstract)
This dissertation consists of three chapters. It develops new methodologies to address two specific problems of recent statistical research: • How to incorporate hierarchical structure in high dimensional regression model selection. • How to achieve semi-parametric efficiency in the presence of missing data. For the first problem, we provide a new approach to explicitly incorporate a given hierarchical structure among the predictors into high dimensional regression model selection. The proposed estimation approach has a hierarchical grouping property so that a pair of variables that are “close” in the hierarchy will be more likely grouped in the estimated model than those that are “far away”. We also prove that the proposed method can consistently select the true model. These properties are demonstrated numerically in simulation and a real data analysis on peripheral-blood mononuclear cell (PBMC) study. For the second problem, two frameworks are considered: generalized partially linear model (GPLM) and causal inference of observational study. Specifically, under the GPLM framework, we consider a broad range of missing patterns which subsume most publications on the same topic. We use the concept of least favorable curve and extend the generalized profile likelihood approach [Severini and Wong (1992)] to estimate the parametric component of the model, and prove that the proposed estimator is consistent and semi-parametrically efficient. Also, under the causal inference framework, we propose to estimate the mean treatment effect with non-randomized treatment exposures in the presence of missing data. An appealing aspect of this development is that we incorporate the post-baseline covariates which are often excluded from causal effect inference due to their inherent confounding effect with treatment. We derive the semiparametric efficiency bound for regular asymptotically linear (RAL) estimators and propose an estimator which achieves this bound. Moreover, we prove that the proposed estimator is robust against four types of model mis-specifications. The performance of the proposed approaches are illustrated numerically through simulations and real data analysis on group testing dataset from Nebraska Infertility Prevention Project and burden of illness dataset from Duke University Medical Center.
Subject (authority = RUETD)
Topic
Statistics and Biostatistics
RelatedItem (type = host)
TitleInfo
Title
Rutgers University Electronic Theses and Dissertations
Identifier (type = RULIB)
ETD
Identifier
ETD_4190
PhysicalDescription
Form (authority = gmd)
electronic resource
InternetMediaType
application/pdf
InternetMediaType
text/xml
Extent
x, 104 p. : ill.
Note (type = degree)
Ph.D.
Note (type = bibliography)
Includes bibliographical references
Note (type = vita)
Includes vita
Note (type = statement of responsibility)
by Wenqian Qiao
Subject (authority = ETD-LCSH)
Topic
Statistics--Research
Subject (authority = ETD-LCSH)
Topic
Statistics--Methodology
Identifier (type = hdl)
http://hdl.rutgers.edu/1782.1/rucore10001600001.ETD.000066941
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/T3HQ3XP8
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
Qiao
GivenName
Wenqian
Role
Copyright Holder
RightsEvent
Type
Permission or license
DateTime (encoding = w3cdtf); (qualifier = exact); (point = start)
2012-08-11 23:11:09
AssociatedEntity
Name
Wenqian Qiao
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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