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

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 interval
• double-censored data
• efron's bootstrap
• iterative methods
• simulation
• smoothed bootstrap