text: 62,161,1481193458161356800,1109044782809206784,Ginette Lafit 💚,"In this new preprint, we (@fjnogales, @ivanmarce1, Ruben Zamar, and me) propose the use of a robust covariance estimator based on multivariate Winsorization for sparse estimation of the precision matrix of a Gaussian graphical model <LINK> 1/5 We investigate the performance of Glasso under cellwise contamination (right panel) which differs from the classical casewise contamination model (left panel) because each cell of a data has a probability to be independently contaminated 2/5 <LINK> In the context of cellwise outliers, traditional robust estimators of the covariance matrix are not well suited because they rely on linear combinations of the observations which have a high probability of being contaminated. This is known as outliers propagation. 3/5 In the presence of cellwise outliers, the Graphical lasso is no longer robust. As a result, we cannot use the precision matrix estimated by Glasso to learn about conditional independence in a Gaussian graphical model. 4/5 Thus we propose a robust estimator of Glasso by plugging in a robust estimator of the covariance matrix based on bivariate winsorization. The proposal has a competitive behavior, regarding the recovery of the graph in comparison with existing approaches. 5/5",https://arxiv.org/abs/2201.03659,"We propose the use of a robust covariance estimator based on multivariate Winsorization in the context of the Tarr-Muller-Weber framework for sparse estimation of the precision matrix of a Gaussian graphical model. Likewise Croux-Ollerer's precision matrix estimator, our proposed estimator attains the maximum finite sample breakdown point of 0.5 under cellwise contamination. We conduct an extensive Monte Carlo simulation study to assess the performance of ours and the currently existing proposals. We find that ours has a competitive behavior, regarding the the estimation of the precision matrix and the recovery of the graph. We demonstrate the usefulness of the proposed methodology in a real application to breast cancer data. ",Robust graphical lasso based on multivariate Winsorization,5,"['In this new preprint, we (@fjnogales, @ivanmarce1, Ruben Zamar, and me) propose the use of a robust covariance estimator based on multivariate Winsorization for sparse estimation of the precision matrix of a Gaussian graphical model\n<LINK>\n1/5', 'We investigate the performance of Glasso under cellwise contamination (right panel) which differs from the classical casewise contamination model (left panel) because each cell of a data has a probability to be independently contaminated\n2/5 https://t.co/MxWNZN9B6k', 'In the context of cellwise outliers, traditional robust estimators of the covariance matrix are not well suited because they rely on linear combinations of the observations which have a high probability of being contaminated. This is known as outliers propagation.\n3/5', 'In the presence of cellwise outliers, the Graphical lasso is no longer robust. As a result, we cannot use the precision matrix estimated by Glasso to learn about conditional independence in a Gaussian graphical model.\n4/5', 'Thus we propose a robust estimator of Glasso by plugging in a robust estimator of the covariance matrix based on bivariate winsorization. The proposal has a competitive behavior, regarding the recovery of the graph in comparison with existing approaches.\n5/5']",22,01,1242