Article
A Fitted Sparse-Group Lasso for Genome-Based Evaluations.
IEEE/ACM transactions on computational biology and bioinformatics - 1 Jan 2000
Klosa Jan, Simon Noah, Liebscher Volkmar, Wittenburg Dorte
Abstract excerpt
In life sciences, high-throughput techniques typically lead to high-dimensional data and often the number of covariates is much larger than the number of observations. This inherently comes with multicollinearity challenging a statistical analysis in a linear regression framework. Penalization methods such as the lasso, ridge regression, the group lasso, and convex combinations thereof, which introduce additional...
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