-
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...
read it
-
Adaptive lasso and Dantzig selector for spatial point processes intensity estimation
Lasso and Dantzig selector are standard procedures able to perform varia...
read it
-
Lasso, knockoff and Gaussian covariates: a comparison
Given data y and k covariates x_j one problem in linear regression is to...
read it
-
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...
read it
-
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 ...
read it
-
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...
read it
-
Robust Lasso-Zero for sparse corruption and model selection with missing covariates
We propose Robust Lasso-Zero, an extension of the Lasso-Zero methodology...
read it
A critical review of LASSO and its derivatives for variable selection under dependence among covariates
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.
READ FULL TEXT
Comments
There are no comments yet.