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Research Article | Open Access

Nonparametric bootstrap methods for hypothesis testing in the event of double-censored data

Department of Mathematics, College of Science, Qassim University, P.O. Box 6644, Buraydah 51452, Saudi Arabia
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Abstract

This paper illustrated how nonparametric bootstrap methods for double-censored data can be used to conduct some hypothesis tests, such as quartiles' hypothesis tests. Through simulation studies, the smoothed bootstrap (SB) method performed better results than Efron's method in most scenarios, particularly for small datasets. The SB method provided smaller discrepancies between the actual and nominal error rates.

Keywords

Bootstrap confidence intervaldouble-censored dataEfron's bootstrapiterative methodssimulationsmoothed bootstrap
CLC number: 62G09

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AIMS Mathematics
Volume 9 Issue 2,
February 2024
Pages 4649-4664
DOI: 10.3934/math.2024224

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Cite this article:
Al Luhayb ASM. Nonparametric bootstrap methods for hypothesis testing in the event of double-censored data. AIMS Mathematics, 2024, 9(2): 4649-4664. https://doi.org/10.3934/math.2024224

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Received: 30 November 2023
Revised: 05 January 2024
Accepted: 12 January 2024
Published: 15 February 2024
©2024 the Author(s), licensee AIMS Press.

This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0)