On the inconsistency of ℓ -penalised sparse precision matrix estimation.

Journal: BMC bioinformatics
Published Date: Dec 13, 2016

Abstract

BACKGROUND: Various ℓ -penalised estimation methods such as graphical lasso and CLIME are widely used for sparse precision matrix estimation and learning of undirected network structure from data. Many of these methods have been shown to be consistent under various quantitative assumptions about the underlying true covariance matrix. Intuitively, these conditions are related to situations where the penalty term will dominate the optimisation.

Authors

  • Otte Heinävaara
    Helsinki Institute for Information Technology HIIT, Department of Computer Science, University of Helsinki, Helsinki, Finland.
  • Janne Leppä-Aho
    Helsinki Institute for Information Technology HIIT, Department of Computer Science, University of Helsinki, Helsinki, Finland.
  • Jukka Corander
    Department of Mathematics and Statistics, University of Helsinki, Helsinki, Finland.
  • Antti Honkela
    Helsinki Institute for Information Technology HIIT, Department of Computer Science, University of Helsinki, Helsinki, Finland. antti.honkela@helsinki.fi.

Keywords

External Resources

Popular Topics
Recent Journals