Weak Convergence of the Wild Bootstrap for the Aalen-Johansen Estimator of the Cumulative Incidence Function of a Competing Risk

We give a rigorous study of weak convergence of the wild bootstrap for non-parametric estimation of the cumulative event probability of a competing risk. The data may be subject to independent left-truncation and right-censoring. Inclusion of left-truncation is motivated by a study on pregnancy outc...

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Bibliographic Details
Published inScandinavian journal of statistics Vol. 40; no. 3; pp. 387 - 402
Main Authors BEYERSMANN, JAN, TERMINI, SUSANNA DI, PAULY, MARKUS
Format Journal Article
LanguageEnglish
Published Oxford, UK Blackwell Publishing Ltd 01.09.2013
Wiley Publishing
Subjects
abortion
Approximation
Bootstrap method
Clinical outcomes
Convergence
Estimating techniques
Estimators
Inclusions
left-truncation
Pregnancy
Probability
Public health
right-censoring
Risk
Simulation
simultaneous confidence bands
Statistical analysis
Statistical methods
Statistics
Studies
Online AccessGet full text
ISSN0303-6898
1467-9469
DOI10.1111/j.1467-9469.2012.00817.x

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Summary:We give a rigorous study of weak convergence of the wild bootstrap for non-parametric estimation of the cumulative event probability of a competing risk. The data may be subject to independent left-truncation and right-censoring. Inclusion of left-truncation is motivated by a study on pregnancy outcomes. The wild bootstrap includes as one case a popular resampling technique, where the limit distribution is approximated by repeatedly generating standard normal variates, while the data are kept fixed. Simulation results and a data example are also presented.
Bibliography:ark:/67375/WNG-VDD8GCHF-K
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ISSN:0303-6898
1467-9469
DOI:10.1111/j.1467-9469.2012.00817.x