Selecting the tuning parameter in penalized Gaussian graphical models

DOI10.1007/s11222-018-9823-5zbMath1430.62020OpenAlexW2821512315MaRDI QIDQ2329783

Angelo M. Mineo, Antonino Abbruzzo, Ivan Vujačić, Ernst C. Wit

Publication date: 18 October 2019

Published in: Statistics and Computing (Search for Journal in Brave)

Full work available at URL: https://research.rug.nl/en/publications/selecting-the-tuning-parameter-in-penalized-gaussian-graphical-models(16692e21-4e05-418c-8e54-1942f8130c9e).html

zbMATH Keywords

model selectionKullback-Leibler divergencepenalized likelihoodgeneralized information criterionmodel complexity

Mathematics Subject Classification ID

Applications of graph theory (05C90) Statistical aspects of information-theoretic topics (62B10) Graphical methods in statistics (62A09)

Related Items

A unified class of penalties with the capability of producing a differentiable alternative to l₁ norm penalty

Uses Software

• GMRFLib
• glasso
• huge
• MIM

Cites Work

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