Bayesian variable selection for correlated covariates via colored cliques

Stefano Monni ()

AStA Advances in Statistical Analysis, 2014, vol. 98, issue 2, 143-163

Abstract: We propose a Bayesian method to select groups of correlated explanatory variables in a linear regression framework. We do this by introducing in the prior distribution assigned to the regression coefficients a random matrix $$G$$ G that encodes the group structure. The groups can thus be inferred by sampling from the posterior distribution of $$G$$ G . We then give a graph-theoretic interpretation of this random matrix $$G$$ G as the adjacency matrix of cliques. We discuss the extension of the groups from cliques to more general random graphs, so that the proposed approach can be viewed as a method to find networks of correlated covariates that are associated with the response. Copyright Springer-Verlag Berlin Heidelberg 2014

Keywords: Variable selection; Graphs (search for similar items in EconPapers)
Date: 2014
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Persistent link: https://EconPapers.repec.org/RePEc:spr:alstar:v:98:y:2014:i:2:p:143-163

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DOI: 10.1007/s10182-013-0218-9

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