Broken adaptive ridge (BAR) is a computationally scalable surrogate to $$L_0$$ -penalized regression, which involves iteratively performing reweighted $$L_2$$ penalized regressions and enjoys some appealing properties of both while avoiding their limitations. In this paper, we extend the BAR method semi-parametric accelerated failure time (AFT) model for right-censored survival data. Specifical...

Kernel ridge regression (KRR) is a well-known and popular nonparametric approach with many desirable properties, including minimax rate-optimality in estimating functions that belong to common reproducing kernel Hilbert spaces (RKHS). The approach, however, computationally intensive for large data sets, due the need operate on dense n×n matrix, where n sample size. Recently, various approximati...

Bone age is an important measure for assessing the skeletal and biological maturity of children. Delayed or increased bone a serious concern pediatricians, needs to be accurately assessed in bid determine whether occurring at rate consistent with chronological age. In this paper, we introduce unified deep learning framework assessment using instance segmentation ridge regression. The proposed a...