Hierarchical Normalized Completely Random Measures for Robust Graphical Modeling.

ABSTRACT: Gaussian graphical models are useful tools for exploring network structures in multivariate normal data. In this paper we are interested in situations where data show departures from Gaussianity, therefore requiring alternative modeling distributions. The multivariate t-distribution, obtained by dividing each component of the data vector by a gamma random variable, is a straightforward generalization to accommodate deviations from normality such as heavy tails. Since different groups of variables may be contaminated to a different extent, Finegold and Drton (2014) introduced the Dirichlet t-distribution, where the divisors are clustered using a Dirichlet process. In this work, we consider a more general class of nonparametric distributions as the prior on the divisor terms, n

SUBMITTER: Cremaschi A 

PROVIDER: S-EPMC7237071 | biostudies-literature | 2019 Dec

REPOSITORIES: biostudies-literature