Non-strange Weird Resampling for Complex Survival Data

This paper introduces the new data-dependent multiplier bootstrap for non-parametric analysis of survival data, possibly subject to competing risks. The new resampling procedure includes both the general wild bootstrap and the weird bootstrap as special cases. The data may be subject to independent right-censoring and left-truncation. We rigorously prove asymptotic correctness which has in particular been pending for the weird bootstrap. As a consequence, pointwise as well as time-simultaneous inference procedures for, amongst others, the classical survival setting are deduced. We report simulation results and a real data analysis of the cumulative cardiovascular event probability. The simulation results suggest that both the weird bootstrap and use of non-standard multipliers in the wild bootstrap may perform preferably.

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