Cheap Subsampling bootstrap confidence intervals for fast and robust
inference in biostatistics
Cheap Subsampling bootstrap confidence intervals for fast and robust
inference in biostatistics
Bootstrapping is often applied to get confidence limits for semiparametric inference of a target parameter in the presence of nuisance parameters. Bootstrapping with replacement can be computationally expensive and problematic when cross-validation is used in the estimation algorithm due to duplicate observations in the bootstrap samples. We provide a valid, …