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》 资源 》论文》On model selection consistency of M-estimators with geometrically decomposable penalties

On model selection consistency of M-estimators with geometrically decomposable penalties

2020-01-16 | |  64 |   35 |   0

Abstract

Penalized M-estimators are used in diverse areas of science and engineering to fit high-dimensional models with some low-dimensional structure. Often, the penalties are geometrically decomposable, i.e. can be expressed as a sum of support functions over convex sets. We generalize the notion of irrepresentable to geometrically decomposable penalties and develop a general framework for establishing consistency and model selection consistency of M-estimators with such penalties. We then use this framework to derive results for some special cases of interest in bioinformatics and statistical learning.

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