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Simultaneous variable selection and ...

Li, Wei.

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Simultaneous variable selection and outlier detection using LASSO with applications to aircraft landing data analysis.

Record Type:	Language materials, printed : Monograph/item
Title/Author:	Simultaneous variable selection and outlier detection using LASSO with applications to aircraft landing data analysis./
Author:	Li, Wei.
Description:	78 p.
Notes:	Source: Dissertation Abstracts International, Volume: 74-02(E), Section: B.
Contained By:	Dissertation Abstracts International74-02B(E).
Subject:	Biology, Biostatistics. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3541172
ISBN:	9781267670861

Simultaneous variable selection and outlier detection using LASSO with applications to aircraft landing data analysis.
Li, Wei.

Simultaneous variable selection and outlier detection using LASSO with applications to aircraft landing data analysis. - 78 p.

Source: Dissertation Abstracts International, Volume: 74-02(E), Section: B.

Thesis (Ph.D.)--Rutgers The State University of New Jersey - New Brunswick, 2012.

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-covariates 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.

ISBN: 9781267670861Subjects--Topical Terms:

1018416
Biology, Biostatistics.

Simultaneous variable selection and outlier detection using LASSO with applications to aircraft landing data analysis.
LDR:02273nam a2200289 4500 001 1958214
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008 150210s2012 ||||||||||||||||| ||eng d
020 $a 9781267670861
035 $a (MiAaPQ)AAI3541172
035 $a AAI3541172
040 $a MiAaPQ $c MiAaPQ
100 1 $a Li, Wei. $3 884990
245 1 0 $a Simultaneous variable selection and outlier detection using LASSO with applications to aircraft landing data analysis.
300 $a 78 p.
500 $a Source: Dissertation Abstracts International, Volume: 74-02(E), Section: B.
500 $a Adviser: Regina Y. Liu.
502 $a Thesis (Ph.D.)--Rutgers The State University of New Jersey - New Brunswick, 2012.
520 $a 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-covariates 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.
590 $a School code: 0190.
650 4 $a Biology, Biostatistics. $3 1018416
650 4 $a Statistics. $3 517247
650 4 $a Applied Mathematics. $3 1669109
690 $a 0308
690 $a 0463
690 $a 0364
710 2 $a Rutgers The State University of New Jersey - New Brunswick. $b Graduate School - New Brunswick. $3 1019196
773 0 $t Dissertation Abstracts International $g 74-02B(E).
790 $a 0190
791 $a Ph.D.
792 $a 2012
793 $a English
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3541172