Objective Bayes Covariate-Adjusted Sparse Graphical Model Selection

Guido Consonni, Stefano Peluso, Luca La Rocca

Risultato della ricerca: Contributo in rivistaArticolo in rivista

14 Citazioni (Scopus)

Abstract

We present an objective Bayes method for covariance selection in Gaussian multivariate regression models having a sparse regression and covariance structure, the latter being Markov with respect to a Directed Acyclic Graph (DAG). Our procedure can be easily complemented with a variable selection step, so that variable and graphical model selection can be performed jointly. In this way, we oer a solution to a problem of growing importance especially in the area of genetical genomics (eQTL analysis). The input of our method is a single default prior, essentially involving no subjective elicitation, while its output is a closed form marginal likelihood for every covariateadjusted DAG model, which is constant over each class of Markov equivalent DAGs; our procedure thus naturally encompasses covariate-adjusted decomposable graphical models. In realistic experimental studies our method is highly competitive, especially when the number of responses is large relative to the sample size.
Lingua originaleEnglish
pagine (da-a)741-764
Numero di pagine24
RivistaScandinavian Journal of Statistics
DOI
Stato di pubblicazionePubblicato - 2017

Keywords

  • Bayesian Model Selection

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