A computational method to estimate sparse multiple Gaussian graphical models

Research output: Contribution to conferenceOther

Abstract

In recent years several researchers have proposed the use of the Gaussian graphical model defined on a high dimensional setting to explore the dependence relationships between random variables. Standard methods, usually proposed in literature, are based on the use of a specific penalty function, such as the L1-penalty function. In this paper our aim is to estimate and compare two or more Gaussian graphical models defined in a high dimensional setting. In order to accomplish our aim, we propose a new computational method, based on glasso method, which lets us to extend the notion of p-value.
Original languageEnglish
Number of pages4
Publication statusPublished - 2012

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