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A note on the Lasso for Gaussian graphical model selection

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  • Meinshausen, Nicolai

Abstract

Inspired by the success of the Lasso for regression analysis, it seems attractive to estimate the graph of a multivariate normal distribution by l1-norm penalized likelihood maximization. We examine some properties of the estimator and show that care has to be taken with interpretation of results as the estimator is not consistent for some graphs.

Suggested Citation

  • Meinshausen, Nicolai, 2008. "A note on the Lasso for Gaussian graphical model selection," Statistics & Probability Letters, Elsevier, vol. 78(7), pages 880-884, May.
    • Handle: RePEc:eee:stapro:v:78:y:2008:i:7:p:880-884
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    References listed on IDEAS

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    1. Ming Yuan & Yi Lin, 2007. "On the non‐negative garrotte estimator," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 69(2), pages 143-161, April.
    2. Ming Yuan & Yi Lin, 2007. "Model selection and estimation in the Gaussian graphical model," Biometrika, Biometrika Trust, vol. 94(1), pages 19-35.
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    Cited by:

    1. A. Gibberd & S. Roy, 2021. "Consistent multiple changepoint estimation with fused Gaussian graphical models," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 73(2), pages 283-309, April.
    2. Nicolai Meinshausen & Peter Bühlmann, 2010. "Stability selection," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 72(4), pages 417-473, September.
    3. Max Hinne & Ronald J Janssen & Tom Heskes & Marcel AJ van Gerven, 2015. "Bayesian Estimation of Conditional Independence Graphs Improves Functional Connectivity Estimates," PLOS Computational Biology, Public Library of Science, vol. 11(11), pages 1-26, November.
    4. Guðmundsson, Guðmundur Stefán & Brownlees, Christian, 2021. "Detecting groups in large vector autoregressions," Journal of Econometrics, Elsevier, vol. 225(1), pages 2-26.

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