# Bootstrap- and permutation-based inference for the Mann–Whitney effect for right-censored and tied data

@article{Dobler2016BootstrapAP, title={Bootstrap- and permutation-based inference for the Mann–Whitney effect for right-censored and tied data}, author={Dennis Dobler and Markus Pauly}, journal={TEST}, year={2016}, volume={27}, pages={639-658} }

The Mann–Whitney effect is an intuitive measure for discriminating two survival distributions. Here we analyse various inference techniques for this parameter in a two-sample survival setting with independent right-censoring, where the survival times are even allowed to be discretely distributed. This allows for ties in the data and requires the introduction of normalized versions of Kaplan–Meier estimators from which adequate point estimates are deduced. Asymptotically exact inference…

#### 16 Citations

Estimation of the Mann-Whitney effect in the two-sample problem under dependent censoring

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A new estimator of the Mann–Whitney effect is proposed by applying the copula-graphic estimator to adjust for the effect of dependent censoring and it is shown that the proposed estimator and test are consistent when the assumed copulas for the two groups are correct.

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