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Simultaneous variable selection and outlier detection using LASSO with applications to aircraft landing data analysis
Descriptive
TitleInfo
Title
Simultaneous variable selection and outlier detection
using LASSO with applications to aircraft landing data
analysis
using LASSO with applications to aircraft landing data
analysis
Name
(type = personal)
NamePart
(type = family)
Li
NamePart
(type = given)
Wei
NamePart
(type = date)
1982-
DisplayForm
Wei Li
Role
RoleTerm
(authority = RULIB)
author
Name
(type = personal)
NamePart
(type = family)
Liu
NamePart
(type = given)
Regina Y.
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Regina Y. Liu
Affiliation
Advisory Committee
Role
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(authority = RULIB)
chair
Name
(type = personal)
NamePart
(type = family)
Xie
NamePart
(type = given)
Minge
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Minge Xie
Affiliation
Advisory Committee
Role
RoleTerm
(authority = RULIB)
internal member
Name
(type = personal)
NamePart
(type = family)
Zhang
NamePart
(type = given)
Cun-Hui
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Cun-Hui Zhang
Affiliation
Advisory Committee
Role
RoleTerm
(authority = RULIB)
internal member
Name
(type = personal)
NamePart
(type = family)
Cheng
NamePart
(type = given)
Andrew
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Andrew Cheng
Affiliation
Advisory Committee
Role
RoleTerm
(authority = RULIB)
outside member
Name
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NamePart
Rutgers University
Role
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(authority = RULIB)
degree grantor
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Graduate School - New Brunswick
Role
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school
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Text
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theses
OriginInfo
DateCreated
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2012
DateOther
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(type = degree)
2012-05
Place
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xx
Language
LanguageTerm
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(type = code)
eng
Abstract
(type = abstract)
We propose a LASSO-type penalized regression method for simultaneous variable selection and outlier detection in high dimensional linear regression. We apply a mean-shift model to incorporate the coefficients associated with the potential outliers by expressing them as different intercept terms. The sparsity assumption is imposed on both X-covariates and the outlier indicator variables. With suitable penalty factors between X-covaraites and the outlier indicators, we show that the proposed method selects a model of the correct order of dimensionality, under the sparse Riesz condition on the correlation of design variables and a joint sparse Reisz condition on the augmented design matrix. We also show that the estimation/prediction of the selected model can be controlled at a level determined by the sizes of the true model, the outliers and the thresholding level. Moreover, the estimation has a positive breakdown point when both the dimension p and the sample size n tend to infinity, and p >> n. We also provide a generalized version for the estimator by adjusting the penalty weight factor. Finally, we apply the proposed method to analyze an aircraft landing performance data set, for identifying the precursors for undesirable landing performance and reducing the risk of runway overruns.
Subject
(authority = RUETD)
Topic
Statistics and Biostatistics
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Title
Rutgers University Electronic Theses and Dissertations
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ETD_4005
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electronic resource
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application/pdf
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text/xml
Extent
vii, 71 p. : ill.
Note
(type = degree)
Ph.D.
Note
(type = bibliography)
Includes bibliographical references
Note
(type = vita)
Includes vita
Note
(type = statement of responsibility)
by Wei Li
Subject
(authority = ETD-LCSH)
Topic
Instrumental variables (Statistics)
Subject
(authority = ETD-LCSH)
Topic
Airplanes--Landing
Subject
(authority = ETD-LCSH)
Topic
Landing aids (Aeronautics)
Identifier
(type = hdl)
http://hdl.rutgers.edu/1782.1/rucore10001600001.ETD.000065195
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Title
Graduate School - New Brunswick Electronic Theses and Dissertations
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rucore19991600001
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NjNbRU
Identifier
(type = doi)
doi:10.7282/T3WM1CBS
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
Li
GivenName
Wei
Role
Copyright Holder
RightsEvent
Type
Permission or license
DateTime
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(qualifier = exact);
(point = start)
2012-04-16 16:46:25
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Name
Wei Li
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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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Statistical Profile