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Robust concentration graph model selection

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  • Gottard, Anna
  • Pacillo, Simona

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.

Suggested Citation

  • Gottard, Anna & Pacillo, Simona, 2010. "Robust concentration graph model selection," Computational Statistics & Data Analysis, Elsevier, vol. 54(12), pages 3070-3079, December.
    • Handle: RePEc:eee:csdana:v:54:y:2010:i:12:p:3070-3079
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    References listed on IDEAS

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