Variable selection of high-dimensional non-parametric nonlinear systems by derivative averaging to avoid the curse of dimensionality

Automatica ◽  
2019 ◽  
Vol 101 ◽  
pp. 138-149 ◽  
Author(s):  
Er-Wei Bai ◽  
Changming Cheng ◽  
Wen-Xiao Zhao
Keyword(s):  
Nonlinear Systems ◽  
Variable Selection ◽  
Curse Of Dimensionality ◽  
High Dimensional ◽  
Selection Of ◽  
Non Parametric

Stable Variable Selection for High-dimensional Genomic Data with Strong Correlations

2021 ◽  
Author(s):  
Reetika Sarkar ◽  
Sithija Manage ◽  
Xiaoli Gao
Keyword(s):  
Variable Selection ◽  
Genomic Data ◽  
High Dimensional ◽  
Strong Correlations ◽  
Computationally Efficient ◽  
Two Stage ◽  
Selection Of Variables ◽  
Level Variable ◽  
Stable Variable ◽  
Selection Of

Abstract Background: High-dimensional genomic data studies are often found to exhibit strong correlations, which results in instability and inconsistency in the estimates obtained using commonly used regularization approaches including both the Lasso and MCP, and related methods. Result: In this paper, we perform a comparative study of regularization approaches for variable selection under different correlation structures, and propose a two-stage procedure named rPGBS to address the issue of stable variable selection in various strong correlation settings. This approach involves repeatedly running of a two-stage hierarchical approach consisting of a random pseudo-group clustering and bi-level variable selection. Conclusion: Both the simulation studies and high-dimensional genomic data analysis have demonstrated the advantage of the proposed rPGBS method over most commonly used regularization methods. In particular, the rPGBS results in more stable selection of variables across a variety of correlation settings, as compared to recent work addressing variable selection with strong correlations. Moreover, the rPGBS is computationally efficient across various settings.

scholarly journals Sharp variable selection of a sparse submatrix in a high-dimensional noisy matrix

2015 ◽  
Vol 19 ◽  
pp. 115-134 ◽  
Author(s):  
Cristina Butucea ◽  
Yuri I. Ingster ◽  
Irina A. Suslina
Keyword(s):  
Variable Selection ◽  
High Dimensional ◽  
Selection Of
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