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M-estimation is a widely used technique for robust statistical inference.In this paper,we study the asymptotic properties of a nonconcave penalized M-estimator in sparse,high-dimensional,linear regression models.Compared with the classic M-estimation,the nonconcave penalized M-estimation method can do parameter estimation and variable selection simultaneously.The proposed method is resistant to heavy-tailed errors or outliers in the response.