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Robust and Sparse Estimation of Linear Regression Coefficients with Heavy-tailed Noises and Covariates. (arXiv:2206.07594v1 [stat.ML])
Robust and sparse estimation of linear regression coefficients is
investigated. The situation addressed by the present paper is that covariates
and noises are sampled from heavy-tailed distributions, and the covariates and
noises are contaminated by malicious outliers. Our estimator can be computed
efficiently. Further, our estimation error bound is sharp.