Robust concentration graph model selection

Authors:
Anna Gottard;Simona Pacillo
Affiliations:
Department of Statistics "G. Parenti", University of Florence, V.le Morgagni 59, Firenze, Italy;Department of PE.ME.IS., University of Sannio, Piazza Arechi II, Benevento, Italy
Venue:
Computational Statistics & Data Analysis
Year:
2010

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Abstract

Concentration graph models are an attractive tool to explore the conditional independence structure in a multivariate normal distribution. In applications, in absence of a priori knowledge, it is possible to select the graph underlying a set of data through an appropriate model selection procedure. The recently proposed procedure, SINful, is appealing but sensitive to outliers, as it utilizes the sample estimator of the covariance matrix. A method to make the SINful procedure robust with respect to the presence of outlying observations, is proposed. This is based on the minimum covariance determinant (MCD) estimator for the variance-covariance matrix. A simulation study shows the advantages of this method.