Staff View
Analysis of big data by split-and-conquer and penalized regressions

Descriptive

TitleInfo
Title
Analysis of big data by split-and-conquer and penalized regressions
SubTitle
new methods and theories
Name (type = personal)
NamePart (type = family)
Chen
NamePart (type = given)
Xueying
NamePart (type = date)
1985-
DisplayForm
Xueying Chen
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)
Zhang
NamePart (type = given)
Cun-Hui
DisplayForm
Cun-Hui Zhang
Affiliation
Advisory Committee
Role
RoleTerm (authority = RULIB)
co-chair
Name (type = personal)
NamePart (type = family)
Dicker
NamePart (type = given)
Lee
DisplayForm
Lee Dicker
Affiliation
Advisory Committee
Role
RoleTerm (authority = RULIB)
internal member
Name (type = personal)
NamePart (type = family)
Lin
NamePart (type = given)
Xiaodong
DisplayForm
Xiaodong Lin
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)
2013
DateOther (qualifier = exact); (type = degree)
2013-01
CopyrightDate (encoding = marc); (point = start); (qualifier = exact)
2013
Place
PlaceTerm (type = code)
xx
Language
LanguageTerm (authority = ISO639-2b); (type = code)
eng
Abstract (type = abstract)
This dissertation develops methodologies for analysis of big data and its related theoretical properties. Recent years, tremendous progress has been made in analysis of big data, especially techniques via penalization and shrinkages. However, there are still many challenging problems to be solved. This dissertation focuses on two settings where (i) the data is too large to fit into a single computer or too expensive to perform a computationally intensive data analysis; or (ii) there are unknown group structures of highly correlated variables. In this dissertation, we first propose a Split-and-Conquer approach to analyze extraordinarily large data. Then, under linear regression settings with highly correlated variables, we investigate model selection properties of OSCAR (octagonal shrinkage and clustering algorithm for regression) estimators (Bondell & Reich, 2008) and propose a more general method Group OSCAR which incorporates both prior knowledge of group structures and correlation patterns among explanatory variables. We first propose a split-and-conquer approach and illustrate it using a computationally intensive penalized regression method. We show that the combined result is asymptotically equivalent to the corresponding analysis result of using the entire data all together. In addition, we demonstrate that the approach has an inherent advantage of being more resistant to false model selections. Furthermore, when a computational intensive algorithm is used, we show that the split-and-conquer approach can substantially reduce computing time and computer memory requirement. Detecting meaningful `groups' of highly correlated variables has been studied a lot. OSCAR estimators provide a feasible way to perform variable selection and clustering simultaneously. However, no theoretical results are provided for OSCAR estimators. In this dissertation, we provide a set of mild conditions under which OSCAR estimators are able to select the true model and keep the order of the coefficients by their magnitudes when the correlations are high. In the last part of this dissertation, we propose a new method. This method not only takes use of known group structures but also incorporates the correlation patterns leading to the underlying unknown group structure. It extends most of the model selections methods in the literature, and has a general grouping effect.
Subject (authority = RUETD)
Topic
Statistics and Biostatistics
RelatedItem (type = host)
TitleInfo
Title
Rutgers University Electronic Theses and Dissertations
Identifier (type = RULIB)
ETD
Identifier
ETD_4452
PhysicalDescription
Form (authority = gmd)
electronic resource
InternetMediaType
application/pdf
InternetMediaType
text/xml
Extent
xi, 116 p. : ill.
Note (type = degree)
Ph.D.
Note (type = bibliography)
Includes bibliographical references
Note (type = vita)
Includes vita
Note (type = statement of responsibility)
by Xueying Chen
Subject (authority = ETD-LCSH)
Topic
Big data
Subject (authority = ETD-LCSH)
Topic
Regression analysis
Identifier (type = hdl)
http://hdl.rutgers.edu/1782.1/rucore10001600001.ETD.000067642
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/T37S7MGK
Genre (authority = ExL-Esploro)
ETD doctoral
Back to the top

Rights

RightsDeclaration (ID = rulibRdec0006)
The author owns the copyright to this work.
RightsHolder (type = personal)
Name
FamilyName
Chen
GivenName
Xueying
Role
Copyright Holder
RightsEvent
Type
Permission or license
DateTime (encoding = w3cdtf); (qualifier = exact); (point = start)
2013-01-01 19:13:41
AssociatedEntity
Name
Xueying Chen
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)
2013-01-31
DateTime (encoding = w3cdtf); (qualifier = exact); (point = end)
2013-08-02
Type
Embargo
Detail
Access to this PDF has been restricted at the author's request. It will be publicly available after August 2nd, 2013.
Copyright
Status
Copyright protected
Availability
Status
Open
Reason
Permission or license
Back to the top

Technical

RULTechMD (ID = TECHNICAL1)
ContentModel
ETD
OperatingSystem (VERSION = 5.1)
windows xp
Back to the top
Version 8.4.8
Rutgers University Libraries - Copyright ©2022