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Weak Convergence of the Wild Bootstrap for the Aalen–Johansen Estimator of the Cumulative Incidence Function of a Competing Risk
Author(s) -
BEYERSMANN JAN,
TERMINI SUSANNA DI,
PAULY MARKUS
Publication year - 2013
Publication title -
scandinavian journal of statistics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.359
H-Index - 65
eISSN - 1467-9469
pISSN - 0303-6898
DOI - 10.1111/j.1467-9469.2012.00817.x
Subject(s) - mathematics , statistics , censoring (clinical trials) , estimator , resampling , weak convergence , econometrics , truncation (statistics) , truncated normal distribution , computer security , computer science , asset (computer security)
. 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.