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We propose the use of a robust covariance estimator based on multivariateWinsorization in the context of the Tarr-Muller-Weber framework for sparseestimation of the precision matrix of a Gaussian graphical model. LikewiseCroux-Ollerer's precision matrix estimator, our proposed estimator attains themaximum finite sample breakdown point of 0.5 under cellwise contamination. Weconduct an extensive Monte Carlo simulation study to assess the performance ofours and the currently existing proposals. We find that ours has a competitivebehavior, regarding the the estimation of the precision matrix and the recoveryof the graph. We demonstrate the usefulness of the proposed methodology in areal application to breast cancer data.

Robust graphical lasso based on multivariate Winsorization