2014/06/23 by Claudio Agostinelli, Andy Leung, Agostinelli, Claudio +6
Decision Sciences · Mathematics · #Advanced Statistical Methods and Models #Advanced Statistical Process Monitoring #Statistical Methods and Inference #math.ST #msc:62G05 #msc:62G35 #stat.TH
paper · pdf · doi:10.48550/arxiv.1406.6031
arxiv created 2014/06/23 · arxiv updated 2014/06/24
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.