Smoothing the Bootstrap for Analysis of Double-Censored Data

Thispaperintroducesanewsmoothedbootstraptechniqueforanalyzingdouble-censored data. The method is implemented based on a variant of Hill’s A (n) assumption adapted for the double-censored setting. Through simulation studies, we compare the proposed approach with Efrons classical bootstrap, focusing on the coverage accuracy of quartiles in bootstrap confidence intervals. The results indicate that the new smoothed bootstrap generally outper- forms Efrons method, particularly for small to medium-sized datasets.