B0203
Title: Smoothed bootstrap method for double-censored data
Authors: Asamh Al Luhayb - Qassim University (Saudi Arabia) [presenting]
Abstract: A smoothed bootstrap method is introduced for double-censored data based on a generalization of Hill's A(n) assumption. The smoothed bootstrap method is compared to Efron's method for double-censored data through simulations. The comparison is conducted in terms of the coverage of percentile confidence intervals for the quartiles. From the study, it is found that the smoothed bootstrap method mostly performs better than Efron's method, in particular for small data sets. We also illustrate the use of the method for survival function inference.
