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Sparse Ridge Fusion For Linear Regression

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Date Issued:
2013
Abstract/Description:
For a linear regression, the traditional technique deals with a case where the number of observations n more than the number of predictor variables p (n(>)p). In the case n(<)p, the classical method fails to estimate the coefficients. A solution of this problem in the case of correlated predictors is provided in this thesis. A new regularization and variable selection is proposed under the name of Sparse Ridge Fusion (SRF). In the case of highly correlated predictor , the simulated examples and a real data show that the SRF always outperforms the lasso, elastic net, and the S-Lasso, and the results show that the SRF selects more predictor variables than the sample size n while the maximum selected variables by lasso is n size.
Title: Sparse Ridge Fusion For Linear Regression.
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Name(s): Mahmood, Nozad, Author
Maboudou, Edgard, Committee Chair
Schott, James, Committee Member
Uddin, Nizam, Committee Member
, Committee Member
University of Central Florida, Degree Grantor
Type of Resource: text
Date Issued: 2013
Publisher: University of Central Florida
Language(s): English
Abstract/Description: For a linear regression, the traditional technique deals with a case where the number of observations n more than the number of predictor variables p (n(>)p). In the case n(<)p, the classical method fails to estimate the coefficients. A solution of this problem in the case of correlated predictors is provided in this thesis. A new regularization and variable selection is proposed under the name of Sparse Ridge Fusion (SRF). In the case of highly correlated predictor , the simulated examples and a real data show that the SRF always outperforms the lasso, elastic net, and the S-Lasso, and the results show that the SRF selects more predictor variables than the sample size n while the maximum selected variables by lasso is n size.
Identifier: CFE0005031 (IID), ucf:49997 (fedora)
Note(s): 2013-12-01
M.S.
Sciences, Statistics
Masters
This record was generated from author submitted information.
Subject(s): Lasso -- Coordinate Descent -- Elastic Net -- Smooth Lasso -- Sparsity -- Collinearity -- High-Dimensional Data -- Variable Selection.
Persistent Link to This Record: http://purl.flvc.org/ucf/fd/CFE0005031
Restrictions on Access: public 2013-12-15
Host Institution: UCF

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