Search Results - subject:"Graphical Gaussian Models"

HIERARCHICAL GRAPHICAL MODELS, WITH APPLICATION TO SYSTEMIC RISK

Ahelegbey, Daniel Felix; Giudici, Paolo - Dipartimento di Scienze Economiche e Aziendali, … - 2014

graphical Gaussian models. From a statistical viewpoint, we propose a new class of hierarchical Bayesian graphical models, that …

Persistent link: https://ebvufind01.dmz1.zbw.eu/10010732703

Financial big data analysis for the estimation of systemic risks

Cerchiello, Paola; Giudici, Paolo - Dipartimento di Scienze Economiche e Aziendali, … - 2014

Systemic risk modelling concerns the estimation of the interrelationships between financial institutions, with the aim of establishing which of them are more central and, therefore, more contagious/subject to contagion. The aim of this paper is to develop a novel systemic risk model. A model...

Persistent link: https://ebvufind01.dmz1.zbw.eu/10010891907

Hierarchical graphical models, with application to systemic risk

Ahelegbey, Daniel Felix; Giudici, Paolo - 2014

Persistent link: https://ebvufind01.dmz1.zbw.eu/10011632007

NetMES : a network based marginal expected shortfall measure

Hashem, Shatha Qamhieh; Giudici, Paolo - In: The journal of network theory in finance 2 (2016) 3, pp. 1-36

Persistent link: https://ebvufind01.dmz1.zbw.eu/10011668564

A note on the lack of symmetry in the graphical lasso

Rolfs, Benjamin T.; Rajaratnam, Bala - In: Computational Statistics & Data Analysis 57 (2013) 1, pp. 429-434

The graphical lasso (glasso) is a widely-used fast algorithm for estimating sparse inverse covariance matrices. The glasso solves an ℓ1 penalized maximum likelihood problem and is available as an R library on CRAN. The output from the glasso, a regularized covariance matrix estimate Σˆglasso...

Persistent link: https://ebvufind01.dmz1.zbw.eu/10011056551

Assessing the Validity Domains of Graphical Gaussian Models in Order to Infer Relationships among Components of Complex Biological Systems

Villers, Fanny; Schaeffer, Brigitte; Bertin, Caroline; … - In: Statistical Applications in Genetics and Molecular Biology 7 (2009) 2, pp. 14-14

types of data and a priori knowledge, methods based on graphical Gaussian models are capable of learning the network …

Persistent link: https://ebvufind01.dmz1.zbw.eu/10005585062

Bayesian inference for graphical factor analysis models

Giudici, Paolo; Stanghellini, Elena - In: Psychometrika 66 (2001) 4, pp. 577-591

Persistent link: https://ebvufind01.dmz1.zbw.eu/10005381938