On the impact of contaminations in graphical Gaussian models |
| |
Authors: | Anna Gottard Simona Pacillo |
| |
Institution: | (1) Department of Statistics “G. Parenti”, University of Florence, Viale Morgagni 59, Florence, 50134, Italy |
| |
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 |
本文献已被 SpringerLink 等数据库收录! |
|