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New models and methods for applied statistics

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TitleInfo
Title
New models and methods for applied statistics
SubTitle
topics in computer experiments and time series analysis
Name (type = personal)
NamePart (type = family)
Zhao
NamePart (type = given)
Yibo
NamePart (type = date)
1988-
DisplayForm
Yibo Zhao
Role
RoleTerm (authority = RULIB)
author
Name (type = personal)
NamePart (type = family)
Hung
NamePart (type = given)
Ying
DisplayForm
Ying Hung
Affiliation
Advisory Committee
Role
RoleTerm (authority = RULIB)
chair
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
CopyrightDate (encoding = w3cdtf); (qualifier = exact)
2017
Place
PlaceTerm (type = code)
xx
Language
LanguageTerm (authority = ISO639-2b); (type = code)
eng
Abstract (type = abstract)
In applied statistics, people develop models to solve real world problems based on data. However, the data is growing fast and become more and more massive and complex. Conventional models are limited in the capability of dealing with the fast growing data. This dissertation develops two new models in computer experiments and time series analysis. The new models are developed based on the special features of two real-world problems. The two datasets are from an IBM data thermal study and a biological cell adhesion experiment. For computer experiment, we address two important issues in Gaussian process (GP) modeling. One is how to reduce the computational complexity in GP modeling and the other is how to simultaneous perform variable selection and estimation for the mean function of GP models. Estimation is computationally intensive for GP models because it heavily involves manipulations of an n-by-n correlation matrix, where n is the sample size. Conventional penalized likelihood approaches are widely used for variable selection. However, the computational cost of the penalized likelihood estimation (PMLE) or the corresponding one-step sparse estimation (OSE) can be prohibitively high as the sample size becomes large, especially for GP models. To address both issues, this article proposes an efficient subsample aggregating (subagging) approach with an experimental design-based subsampling scheme. The proposed method is computationally cheaper, yet it can be shown that the resulting subagging estimators achieve the same efficiency as the original PMLE and OSE asymptotically. The finite-sample performance is examined through simulation studies. Application of the proposed methodology to a data center thermal study reveals some interesting information, including identifying an efficient cooling mechanism. Motivated by an analysis of cell adhesion experiments, we introduce a new statistical framework within which the unique features are incorporated and the molecular binding mechanism can be studied. This framework is based upon an extension of Markov switching autoregressive (MSAR) models, a regime-switching type of time series model generalized from hidden Markov models. Standard MSAR models are developed for the analysis of individual stochastic process. To handle multiple time series processes, we introduce Markov switching autoregressive mixed (MSARM) model that simultaneously models multiple time series processes collected from different experimental subjects as in the longitudinal data setting. More than a simple extension, the MSARM model posts statistical challenges in the theoretical developments as well as computational efficiency in high-dimensional integration.
Subject (authority = RUETD)
Topic
Statistics and Biostatistics
RelatedItem (type = host)
TitleInfo
Title
Rutgers University Electronic Theses and Dissertations
Identifier (type = RULIB)
ETD
Identifier
ETD_8331
PhysicalDescription
Form (authority = gmd)
electronic resource
InternetMediaType
application/pdf
InternetMediaType
text/xml
Extent
1 online resource (ix, 64 p. : ill.)
Note (type = degree)
Ph.D.
Note (type = bibliography)
Includes bibliographical references
Subject (authority = ETD-LCSH)
Topic
Gaussian processes
Note (type = statement of responsibility)
by Yibo Zhao
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/T3NZ8BTK
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
Zhao
GivenName
Yibo
Role
Copyright Holder
RightsEvent
Type
Permission or license
DateTime (encoding = w3cdtf); (qualifier = exact); (point = start)
2017-09-07 23:27:38
AssociatedEntity
Name
Yibo Zhao
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.
Copyright
Status
Copyright protected
Availability
Status
Open
Reason
Permission or license
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Technical

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DateCreated (point = end); (encoding = w3cdtf); (qualifier = exact)
2017-10-10T03:44:29
DateCreated (point = end); (encoding = w3cdtf); (qualifier = exact)
2017-10-10T03:44:29
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