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On the impact of contaminations in graphical Gaussian models

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  • Anna Gottard

    (University of Florence)

  • Simona Pacillo

    (University of Florence)

Abstract

This paper analyzes the impact of some kinds of contaminant on model selection in graphical Gaussian models. We investigate four different kinds of contaminants, in order to consider the effect of gross errors, model deviations, and model misspecification. The aim of the work is to assess against which kinds of contaminant a model selection procedure for graphical Gaussian models has a more robust behavior. The analysis is based on simulated data. The simulation study shows that relatively few contaminated observations in even just one of the variables can have a significant impact on correct model selection, especially when the contaminated variable is a node in a separating set of the graph.

Suggested Citation

  • Anna Gottard & Simona Pacillo, 2007. "On the impact of contaminations in graphical Gaussian models," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 15(3), pages 343-354, February.
    • Handle: RePEc:spr:stmapp:v:15:y:2007:i:3:d:10.1007_s10260-006-0041-5
    DOI: 10.1007/s10260-006-0041-5
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    References listed on IDEAS

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    1. V. Fonseca & N. Fieller, 2006. "Distortion in statistical inference: the distinction between data contamination and model deviation," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 63(2), pages 169-190, April.
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    5. A. Capitanio & A. Azzalini & E. Stanghellini, 2003. "Graphical models for skew‐normal variates," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 30(1), pages 129-144, March.
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    Cited by:

    1. Gottard, Anna & Pacillo, Simona, 2010. "Robust concentration graph model selection," Computational Statistics & Data Analysis, Elsevier, vol. 54(12), pages 3070-3079, December.

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