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Thesis

Reweighting methods in high dimensional regression

Abstract:

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...

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Institution:
University of Oxford
Division:
MPLS
Department:
Statistics
Oxford college:
Corpus Christi College
Role:
Author

Contributors

Division:
MPLS
Department:
Statistics
Role:
Supervisor
More from this funder
Name:
Engineering and Physical Sciences Research Council
Funding agency for:
Fang, Z
Publication date:
2012
Type of award:
DPhil
Level of award:
Doctoral
Awarding institution:
University of Oxford
Language:
English
Keywords:
Subjects:
UUID:
uuid:26f8541a-9e2d-466a-84aa-e6850c4baba9
Local pid:
ora:7122
Deposit date:
2013-07-30

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