Author

Y. Nishiyama

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Preprint Sep 2026

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

This paper establishes a unified non-linear regularization framework for high-dimensional $M$-estimation, encompassing both linear regression and Cox's proportional hazards models. Rather than relying on traditional additive non-convex penalties, the proposed paradigm embeds sparsity directly into the parameter mapping...

Y. Nishiyama · 0 citations

Preprint Sep 2026

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

A unified non-linear regularization framework for high-dimensional $M$-estimation, encompassing both linear models and Cox's proportional hazards models, and establishes a unified oracle equivalence for both model classes under a liberated micro-penalty scaling regime.

Y. Nishiyama · 0 citations