Robust Covariance and Scatter Matrix Estimation under Huber's Contamination Model

@article{Chen2015RobustCA,
  title={Robust Covariance and Scatter Matrix Estimation under Huber's Contamination Model},
  author={Mengjie Chen and Chao Gao and Zhao Ren},
  journal={arXiv: Statistics Theory},
  year={2015}
}
Covariance matrix estimation is one of the most important problems in statistics. To accommodate the complexity of modern datasets, it is desired to have estimation procedures that not only can incorporate the structural assumptions of covariance matrices, but are also robust to outliers from arbitrary sources. In this paper, we define a new concept called matrix depth and then propose a robust covariance matrix estimator by maximizing the empirical depth function. The proposed estimator is… Expand
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