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Solar: a least-angle regression for accurate and stable variable selection in high-dimensional data
We propose a new least-angle regression algorithm for variable selection...
07/30/2020 ∙ by Ning Xu, et al. ∙
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Adaptive lasso and Dantzig selector for spatial point processes intensity estimation
Lasso and Dantzig selector are standard procedures able to perform varia...
01/11/2021 ∙ by Achmad Choiruddin, et al. ∙
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Lasso, knockoff and Gaussian covariates: a comparison
Given data y and k covariates x_j one problem in linear regression is to...
05/04/2018 ∙ by Laurie Davies, et al. ∙
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Don't Fall for Tuning Parameters: Tuning-Free Variable Selection in High Dimensions With the TREX
Lasso is a seminal contribution to high-dimensional statistics, but it h...
04/02/2014 ∙ by Johannes Lederer, et al. ∙
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Accuracy and stability of solar variable selection comparison under complicated dependence structures
In this paper we focus on the variable-selection peformance of solar on ...
07/30/2020 ∙ by Ning Xu, et al. ∙
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A statistical methodology to select covariates in high-dimensional data under dependence. Application to the classification of genetic profiles in oncology
We propose a new methodology for selecting and ranking covariates associ...
09/12/2019 ∙ by Bérangère Bastien, et al. ∙
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Robust Lasso-Zero for sparse corruption and model selection with missing covariates
We propose Robust Lasso-Zero, an extension of the Lasso-Zero methodology...
05/12/2020 ∙ by Pascaline Descloux, et al. ∙
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A critical review of LASSO and its derivatives for variable selection under dependence among covariates
12/21/2020 ∙ by Laura Freijeiro-González, et al. ∙
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We study the limitations of the well known LASSO regression as a variable selector when there exists dependence structures among covariates. We analyze both the classic situation with n≥ p and the high dimensional framework with p>n. Restrictive properties of this methodology to guarantee optimality, as well as the inconveniences in practice, are analyzed. Examples of these drawbacks are showed by means of a extensive simulation study, making use of different dependence scenarios. In order to search for improvements, a broad comparison with LASSO derivatives and alternatives is carried out. Eventually, we give some guidance about what procedures are the best in terms of the data nature.
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