OICC Press Mathematical Sciences 2251-7456 19 3 2025 09 30 Smoothing the Bootstrap for Analysis of Double-Censored Data 10.57647/mathsci.2025.1901.01 EN Reid Alotaibi Department of Mathematics, College of Science and Humanities, Shaqra University, Shaqra, Saudi Arabia https://orcid.org/0009-0001-9582-4007 Abdulrahman M. A. Aldawsari Department of Mathematics, College of Sciences and Humanities, Prince Sattam Bin Abdulaziz University, Al-Kharj 16273, Saudi Arabia https://orcid.org/0009-0002-4978-0055 Asamh Saleh M. Al Luhayb Department of Mathematics, College of Science, Qassim University, P.O. Box 6644, Buraydah 51452, Saudi Arabia https://orcid.org/0000-0002-1272-1265 Journal Article 2025 09 30 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.