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- W2950996930 abstract "The validity of various bootstrapping methods has been proved for the sample mean of strongly mixing data. But in many applications, there appear nonlinear statistics of processes that are not strongly mixing. We investigate the nonoverlapping block bootstrap sequences which are near epoch dependent on strong mixing or absolutely regular processes. This includes ARMA and GARCH-processes as well as data from chaotic dynamical systems. We establish the strong consistency of the bootstrap distribution estimator not only for the sample mean, but also for U-statistics, which include examples as Gini's mean difference or the chi^2-test statistic." @default.
- W2950996930 created "2019-06-27" @default.
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- W2950996930 date "2009-11-16" @default.
- W2950996930 modified "2023-09-27" @default.
- W2950996930 title "Bootstrap for the Sample Mean and for U-Statistics of Mixing and Near Epoch Dependent Processes" @default.
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