A0168
Title: The influence function of graphical lasso estimators
Authors: Gaetan Louvet - Universite de Namur (Belgium)
Jakob Raymaekers - KU Leuven (Belgium)
Germain Van Bever - Universite de Namur (Belgium)
Ines Wilms - Maastricht University (Netherlands) [presenting]
Abstract: The precision matrix that encodes conditional linear dependency relations among a set of variables forms an important object of interest in multivariate analysis. Sparse estimation procedures for precision matrices, such as the graphical lasso (Glasso), gained popularity as they facilitate interpretability, thereby separating pairs of variables that are conditionally dependent from those that are independent (given all other variables). Glasso lacks, however, robustness to outliers. To overcome this problem, one typically applies a robust plug-in procedure where the Glasso is computed from a robust covariance estimate instead of the sample covariance, thereby providing protection against outliers. In this talk, we study such estimators theoretically by deriving and comparing their influence function, sensitivity curves and asymptotic variances.
