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This paper considers robust modeling of the survival time for cancerpatients. Accurate prediction can be helpful for developing therapeutic andcare strategies. We propose a unified Expectation-Maximization approachcombined with the L1-norm penalty to perform variable selection and obtainparameter estimation simultaneously for the accelerated failure time model withright-censored survival data. Our approach can be used with general lossfunctions, and reduces to the well-known Buckley-James method when thesquared-error loss is used without regularization. To mitigate the effects ofoutliers and heavy-tailed noise in the real application, we advocate the use ofrobust loss functions under our proposed framework. Simulation studies areconducted to evaluate the performance of the proposed approach with differentloss functions, and an application to an ovarian carcinoma study is provided.Meanwhile, we extend our approach by incorporating the group structure ofcovariates.

Robust Estimation and Variable Selection for the Accelerated Failure Time Model