Analysis of Bootstrap and Subsampling in High-dimensional Regularized Regression

arXiv:2402.13622v1 Announce Type: cross
Abstract: We investigate popular resampling methods for estimating the uncertainty of statistical models, such as subsampling, bootstrap and the jackknife, and their performance in high-dimensional supervised regression tasks. We provide a tight asymptotic description of the biases and variances estimated by these methods in the context of generalized linear models, such as ridge and logistic regression, taking the limit where the number of samples $n$ and dimension $d$ of the covariates grow at a comparable fixed …

abstract analysis arxiv biases bootstrap cond-mat.dis-nn context cs.lg performance popular regression resampling statistical stat.ml tasks type uncertainty