Discussion of “The power of monitoring: how to make the most of a contaminated multivariate sample”

@article{Raymaekers2018DiscussionO,
  title={Discussion of “The power of monitoring: how to make the most of a contaminated multivariate sample”},
  author={Jakob Raymaekers and Peter J. Rousseeuw and Iwein Vranckx},
  journal={Statistical Methods \& Applications},
  year={2018},
  volume={27},
  pages={589-594}
}
In this comment on the discussion paper “The power of monitoring: how to make the most of a contaminated multivariate sample” by A. Cerioli, M. Riani, A. Atkinson and A. Corbellini, we describe how the hard rejection property of the MCD method can be mimicked by an S-estimator with appropriate rho-function. We also point the reader to fast and deterministic algorithms for the MCD, S- and MM-estimators that are specifically suited for monitoring experiments. They were made available a few years… 
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3 Citations
Methods Citations
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3 Citations

Efficient robust methods via monitoring for clustering and multivariate data analysis
Penalised robust estimators for sparse and high-dimensional linear models
TLDR
A new class of robust M -estimators for performing simultaneous parameter estimation and variable selection in high-dimensional regression models and a fast accelerated proximal gradient algorithm, of coordinate descent type, is proposed and implemented for computing the estimates.
Real-time outlier detection for large datasets by RT-DetMCD

References

SHOWING 1-5 OF 5 REFERENCES
Shape bias of robust covariance estimators: an empirical study
Detecting outliers in a multivariate point cloud is not trivial, especially when dealing with a sizable fraction of contamination. Over time, it has increasingly been recognized that the safest and…
A Deterministic Algorithm for Robust Location and Scatter
TLDR
This article presents a deterministic algorithm, denoted as DetMCD, which computes a small number of deterministic initial estimators, followed by concentration steps, and is permutation invariant and very close to affine equivariant.
FSDA: A MATLAB toolbox for robust analysis and interactive data exploration
The DetS and DetMM estimators for multivariate location and scatter
ROBPCA: A New Approach to Robust Principal Component Analysis
TLDR
The ROBPCA approach, which combines projection pursuit ideas with robust scatter matrix estimation, yields more accurate estimates at noncontaminated datasets and more robust estimates at contaminated data.