Publication
Title
Handling cellwise outliers by sparse regression and robust covariance
Author
Abstract
We propose a data-analytic method for detecting cellwise outliers. Given a robust covariance matrix, outlying cells (entries) in a row are found by the cellFlagger technique which combines lasso regression with a stepwise application of constructed cutoff values. The penalty term of the lasso has a physical interpretation as the total distance that suspicious cells need to move in order to bring their row into the fold. For estimating a cellwise robust covariance matrix we construct a detection-imputation method which alternates between flagging outlying cells and updating the covariance matrix as in the EM algorithm. The proposed methods are illustrated by simulations and on real data about volatile organic compounds in children.
Language
English
Source (journal)
Journal of data science, statistics, and visualisation
Publication
2021
DOI
10.52933/JDSSV.V1I3.18
Volume/pages
1 :3 (2021) , p. 1-30
Full text (Publisher's DOI)
Full text (open access)
UAntwerpen
Research group
Publication type
Subject
External links
Record
Identifier
Creation 27.02.2024
Last edited 28.02.2024
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