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

Jason D. Lee, Yuekai Sun, Jonathan E. Taylor

Research output: Contribution to journalConference article

4 Scopus citations

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.

Original languageEnglish (US)
JournalAdvances in Neural Information Processing Systems
StatePublished - Jan 1 2013
Externally publishedYes
Event27th Annual Conference on Neural Information Processing Systems, NIPS 2013 - Lake Tahoe, NV, United States
Duration: Dec 5 2013Dec 10 2013

All Science Journal Classification (ASJC) codes

  • Computer Networks and Communications
  • Information Systems
  • Signal Processing

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