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Contributions to crossover designs and quantile analysis for computer experiments

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TitleInfo
Title
Contributions to crossover designs and quantile analysis for computer experiments
Name (type = personal)
NamePart (type = family)
Wang
NamePart (type = given)
Liwei
NamePart (type = date)
1987-
DisplayForm
Liwei Wang
Role
RoleTerm (authority = RULIB)
author
Name (type = personal)
NamePart (type = family)
Hung
NamePart (type = given)
Ying
DisplayForm
Ying Hung
Affiliation
Advisory Committee
Role
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chair
Name (type = personal)
NamePart (type = family)
DasGupta
NamePart (type = given)
Tirthankar
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Tirthankar DasGupta
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Advisory Committee
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internal member
Name (type = personal)
NamePart (type = family)
Buyske
NamePart (type = given)
Steve
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Steve Buyske
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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
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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 (encoding = w3cdtf); (keyDate = yes); (qualifier = exact)
2019
DateOther (encoding = w3cdtf); (qualifier = exact); (type = degree)
2019-10
Language
LanguageTerm (authority = ISO 639-3:2007); (type = text)
English
Abstract (type = abstract)
This dissertation develops methodologies for optimal crossover designs and quantile modeling in computer experiments. It consists of two parts. The fist part of the dissertation is regarding finding optimal crossover designs for qualitative treatment effect. The second part of the dissertation is to investigate quantile analysis in computer experiments.

Crossover designs for quantitative variables are common in practice but the theoretical developments are overlooked in the design literature. Motivated by an experimental design problem in cell biology. new classes of optimal crossover designs are introduced under different model assumptions on carryover effects with quantitative variables. Theoretical properties of optimal designs are derived which are different from their counterparts with qualitative variables. To efficiently construct optimal designs, systematic procedures are proposed based on a collection of swap operations. The proposed optimal designs are demonstrated by simulation studies and an application in cell adhesion experiments.

The characteristic of computer experiments are that they are deterministic, time-consuming and involve large number of variables. Due to these features of computer experiments, Gaussian process model is a widely used interpolator as an emulator in computer experiments to model mean structure of response. It is also of interest to model different quantiles. Motivated by finding an analogy to Gaussian process models for quantile analysis in computer experiments, a new Bayesian quantile analysis model for computer experiments is introduced. New model developed could capture the non-linearity and smoothness of underlying quantile functions. Quantile predictions in the new model for observed inputs agree on true quantiles asymptotically. With some constraints, asymptotic consistency of coefficients estimation is derived. There is no issue of quantile curves crossing each other in proposed model for quantile prediction of observed inputs.
Subject (authority = local)
Topic
Carryover 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_10094
PhysicalDescription
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application/pdf
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text/xml
Extent
1 online resource (x, 56 pages) : illustrations
Note (type = degree)
Ph.D.
Note (type = bibliography)
Includes bibliographical references
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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/t3-kvtk-rg65
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
Wang
GivenName
Liwei
Role
Copyright Holder
RightsEvent
Type
Permission or license
DateTime (encoding = w3cdtf); (qualifier = exact); (point = start)
2019-06-25 21:24:04
AssociatedEntity
Name
Liwei Wang
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
Type
Embargo
DateTime (encoding = w3cdtf); (qualifier = exact); (point = start)
2019-10-31
DateTime (encoding = w3cdtf); (qualifier = exact); (point = end)
2020-10-30
Detail
Access to this PDF has been restricted at the author's request. It will be publicly available after October 30th, 2020.
Copyright
Status
Copyright protected
Availability
Status
Open
Reason
Permission or license
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