In this thesis, we focus on the application of covariate reweighting with Lasso-style methods for regression in high dimensions, particularly where p ≥n. We apply a particular focus to the case of sparse regression under a-priori grouping structures.
In such problems, even in the linear case, accurate estimation is difficult. Various authors have suggested ideas such as the Group Lasso and the Sparse Group Lasso, based on convex penalties, or alternatively methods like the...Expand abstract
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- Oxford University, UK
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- Zhou Fang
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Reweighting methods in high dimensional regression
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