Robust estimation of multivariate location and scatter in the presence of cellwise and casewise contamination

Claudio Agostinelli, Andy Leung (), Victor Yohai and Ruben Zamar ()

TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, 2015, vol. 24, issue 3, 461 pages

Abstract: Multivariate location and scatter matrix estimation is a cornerstone in multivariate data analysis. We consider this problem when the data may contain independent cellwise and casewise outliers. Flat data sets with a large number of variables and a relatively small number of cases are common place in modern statistical applications. In these cases, global down-weighting of an entire case, as performed by traditional robust procedures, may lead to poor results. We highlight the need for a new generation of robust estimators that can efficiently deal with cellwise outliers and at the same time show good performance under casewise outliers. Copyright Sociedad de Estadística e Investigación Operativa 2015

Keywords: Robust estimation; Multivariate location and scatter; Multivariate data analysis; Cellwise contamination; 62G35; 62G05 (search for similar items in EconPapers)
Date: 2015
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Citations: View citations in EconPapers (17)

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Persistent link: https://EconPapers.repec.org/RePEc:spr:testjl:v:24:y:2015:i:3:p:441-461

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DOI: 10.1007/s11749-015-0450-6

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