Open Conference Systems, 50th Scientific meeting of the Italian Statistical Society

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An extension of the glasso estimator to multivariate censored data
Antonino Abbruzzo, Luigi Augugliaro, Angelo Mineo

Last modified: 2018-05-18


Glasso is one of the most used estimators for inferring genetic networks. Despite its diffusion, there are several fields in applied research where the limits of detection of modern measurement technologies make the use of this estimator theoretically unfounded, even when the assumption of a multivariate Gaussian distribution is satisfied. In this paper we propose an extension to censored data.


1. Friedman, J. H., Hastie T., Tibshiran, T.: Sparce inverse covariance estimation with the graphical lasso. 9(3), 432–441 (2008)

2. Gardner T. S., di Bernardo D., Lorenz D., Collins J. J.: Inferring genetic networks and identifying compound mode of action via expression profiling. Science. 301, 102–105 (2003)

3. Lauritzen, S. L.: Graphical Models. Oxford University Press, Oxford (1996)

4. Little, R. J. A., Rubin, D. B.: Statistical Analysis with Missing Data. JohnWiley & Sons, Inc., Hoboken (2002)

5. Schäfer, J., Strimmer, K.: A shrinkage approach to large-scale covariance matrix estimation

and implications for functional genomics. Statistical Applications in Genetics and Molecular Biology. 4(1). (2005)

6. Städler, N., Bühlmann, P.: Missing values: sparse inverse covariance estimation and an extension to sparse regression. Stat. Comput. 22(1), 219–235 (2012)

7. Uhler C.: Geometry of maximum likelihood estimation in Gaussian graphical models. Ann. Statist. 40(12), 238–261 (2012)

8. Yuan M., Lin, Y.: Model selection and estimation in the Gaussian graphical model. Biometrika. 94(1), 19–35 (2007)

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