Y. Nishiyama

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 models and Cox's proportional hazards models. Rather than relying on traditional additive non-convex penalties, the proposed paradigm embeds sparsity directly into the transformation for th...