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In this paper, we propose variable selection and estimation in generalized linear models using the seamless (SELO) penalized likelihood approach. The SELO penalty is a smooth function that very closely resembles the discontinuous penalty. We develop an efficient algorithm to fit the model, and show that the SELO‐GLM procedure has the oracle property in the presence of a diverging number of...
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