The conditional 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.
Luigi Augugliaro, Gianluca Sottile, Veronica Vinciotti (2019). An extension of the censored gaussian lasso estimator. In Smart Statistics for Smart Applications - Book of Short Papers SIS2019 (pp. 39-46). Milano : Arbia, G; Peluso, S; Pini, A; Rivellini, G.
An extension of the censored gaussian lasso estimator
Luigi Augugliaro
;Gianluca Sottile;
2019-01-01
Abstract
The conditional 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.
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