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

• Claudio Agostinelli ^[3] ; Andy Leung ^[1] ; Victor J. Yohai ^[2] ; Ruben H. Zamar ^[1]
  1. [1] University of British Columbia

     University of British Columbia

     Canadá

     
  2. [2] Universidad de Buenos Aires

     Universidad de Buenos Aires

     Argentina

     
  3. [3] Università Ca’ Foscari di Venezia

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• Localización: Test: An Official Journal of the Spanish Society of Statistics and Operations Research, ISSN-e 1863-8260, ISSN 1133-0686, Vol. 24, Nº. 3, 2015, págs. 441-461
• Idioma: inglés
• DOI: 10.1007/s11749-015-0450-6
• Texto completo no disponible (Saber más ...)
• Resumen

  • 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.