graphiclasso: Graphical lasso for learning sparse inverse-covariance matrices

Aramayis Dallakyan ()
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Aramayis Dallakyan: StataCorp

Stata Journal, 2022, vol. 22, issue 3, 625-642

Abstract: In modern multivariate statistics, where high-dimensional datasets are ubiquitous, learning large (inverse-) covariance matrices is imperative for data analysis. A popular approach to estimating a large inverse-covariance matrix is to regularize the Gaussian log-likelihood function by imposing a convex penalty function. In a seminal article, Friedman, Hastie, and Tibshirani (2008, Biostatis- tics 9: 432–441) proposed a graphical lasso (Glasso) algorithm to efficiently esti- mate sparse inverse-covariance matrices from the convex regularized log-likelihood function. In this article, I first explore the Glasso algorithm and then introduce a new graphiclasso command for the large inverse-covariance matrix estima- tion. Moreover, I provide a useful command for tuning parameter selection in the Glasso algorithm using the extended Bayesian information criterion, the Akaike information criterion, and cross-validation. I demonstrate the use of Glasso using simulation results and real-world data analysis.

Keywords: graphiclasso; graphiclassocv; graphiclassoplot; datafromicov; compareicov; graphical lasso; graphical models; inverse-covariance matrix (search for similar items in EconPapers)
Date: 2022
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DOI: 10.1177/1536867X221124538

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