LASSO-Type Variable Selection Methods for High-Dimensional Data

Guang Hui Fu; Pan Wang

doi:10.4028/www.scientific.net/AMM.444-445.604

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Application of Vortex Lattice Method and Surface Spline Function in Terrain-Induced Wind Field
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Suppression Effect of Jet Flow on Aerodynamic Noise of 3D Cavity
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Studies on Evaluation Methods of Medium-Term Inspection about Master Degree Thesis with Interval Number's Sorting Way
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LASSO-Type Variable Selection Methods for High-Dimensional Data
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On the Construction of Nonnegative 5×5 Matrices from Spectrum Data
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The Inverse Eigenvalue Problem for Symmetric Doubly Arrow Matrices
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HomeApplied Mechanics and MaterialsApplied Mechanics and Materials Vols. 444-445LASSO-Type Variable Selection Methods for...

LASSO-Type Variable Selection Methods for High-Dimensional Data

Abstract:

LASSO is a very useful variable selection method for high-dimensional data , But it does not possess oracle property [Fan and Li, 200 and group effect [Zou and Hastie, 200. In this paper, we firstly review four improved LASSO-type methods which satisfy oracle property and (or) group effect, and then give another two new ones called WFEN and WFAEN. The performance on both the simulation and real data sets shows that WFEN and WFAEN are competitive with other LASSO-type methods.

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Periodical:

Applied Mechanics and Materials (Volumes 444-445)

Pages:

604-609

DOI:

https://doi.org/10.4028/www.scientific.net/AMM.444-445.604

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Online since:

October 2013

Authors:

Guang Hui Fu, Pan Wang

Keywords:

Group Effect, High-Dimensional Data, Lasso, Oracle Property, Variable Selection

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