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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.

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
正題名/作者:	Simultaneous variable selection and outlier detection using LASSO with applications to aircraft landing data analysis./
作者:	Li, Wei.
面頁冊數:	78 p.
附註:	Source: Dissertation Abstracts International, Volume: 74-02(E), Section: B.
Contained By:	Dissertation Abstracts International74-02B(E).
標題:	Biology, Biostatistics. -
電子資源:	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.
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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.
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