On the impact of contaminations in graphical Gaussian models

Anna Gottard () and Simona Pacillo ()
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Anna Gottard: University of Florence
Simona Pacillo: University of Florence

Statistical Methods & Applications, 2007, vol. 15, issue 3, No 5, 343-354

Abstract: 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.

Keywords: Concentration graph models; Contaminants; Graphical models selection; Model deviation; Multivariate normal distribution; Robustness (search for similar items in EconPapers)
Date: 2007
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Citations: View citations in EconPapers (1)

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DOI: 10.1007/s10260-006-0041-5

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