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Oracle high-dimensional $M$-estimation using smooth reparameterization for sparsity

Nishiyama, Yoichi
arXiv (All) · Papers
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62f1262j07methodologystatistics-theory
This paper establishes a unified non-linear regularization framework for high-dimensional $M$-estimation, encompassing both linear models and Cox's proportional hazards models. Rather than relying on traditional additive non-convex penalties which pose severe optimization challenges, the proposed pa
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