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Multivariate Location and Scatter Matrix Estimation Under Cellwise and Casewise Contamination

We consider the problem of multivariate location and scatter matrix estimation when the data contain cellwise and casewise outliers. Agostinelli et al. (2015) propose a two-step approach to deal with this problem: first, apply a univariate filter to remove cellwise outliers and second, apply a generalized S-estimator to downweight casewise outliers. We improve this proposal in three main directions. First, we introduce a consistent bivariate filter to be used in combination with the univariate filter in the first step. Second, we propose a new fast subsampling procedure to generate starting points for the generalized S-estimator in the second step. Third, we consider a non-monotonic weight function for the generalized S-estimator to better deal with casewise outliers in high dimension. A simulation study and real data example show that, unlike the original two-step procedure, the modified two-step approach performs and scales well for high dimension. Moreover, the modified procedure outperforms the original one and other state-of-the-art robust procedures under cellwise and casewise data contamination.