10.57647/mathsci.2025.1901.01

Smoothing the Bootstrap for Analysis of Double-Censored Data

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  • Reid Alotaibi1,
  • Abdulrahman M. A. Aldawsari2,
  • Asamh Saleh M. Al Luhayb*3,
  1. Department of Mathematics, College of Science and Humanities, Shaqra University, Shaqra, Saudi Arabia
  2. Department of Mathematics, College of Sciences and Humanities, Prince Sattam Bin Abdulaziz University, Al-Kharj 16273, Saudi Arabia
  3. Department of Mathematics, College of Science, Qassim University, P.O. Box 6644, Buraydah 51452, Saudi Arabia

Received: 2025-07-02

Revised: 2025-09-17

Accepted: 2025-09-27

Published in Issue 2025-09-30

Copyright (c) 2025 Reid Alotaibi, Abdulrahman M. A. Aldawsari, Asamh Saleh M. Al Luhayb (Author)

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

How to Cite

Alotaibi, R., M. A. Aldawsari, A., & M. Al Luhayb, A. S. (2025). Smoothing the Bootstrap for Analysis of Double-Censored Data. Mathematical Sciences, 19(3). https://doi.org/10.57647/mathsci.2025.1901.01
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Abstract

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.

Keywords

  • Bootstrapping,
  • confidence intervals,
  • doubly-censored data,
  • statistical inference,
  • statis- tical modelling
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