Publication
Title
The influence function of graphical lasso estimators
Author
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. These estimators are studied theoretically, by deriving and comparing their influence function, sensitivity curve and asymptotic variance.
Language
English
Source (journal)
Econometrics and statistics. - Amsterdam, 2017, currens
Publication
Amsterdam : Elsevier , 2023
ISSN
2468-0389 [print]
2452-3062 [online]
DOI
10.1016/J.ECOSTA.2023.03.004
Volume/pages
(2023) , p. 1-13
Full text (Publisher's DOI)
Full text (publisher's version - intranet only)
UAntwerpen
Research group
Publication type
Subject
External links
Record
Identifier
Creation 27.02.2024
Last edited 28.02.2024
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