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• W2018754032 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 linear processes and conditional heteroskedastic 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 such examples as Gini's mean difference or the χ2-test statistic." @default.
• W2018754032 created "2016-06-24" @default.
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• W2018754032 date "2012-06-01" @default.
• W2018754032 modified "2023-10-02" @default.
• W2018754032 title "Bootstrap for the sample mean and for<i>U</i>-statistics of mixing and near-epoch dependent processes" @default.
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• W2018754032 doi "https://doi.org/10.1080/10485252.2012.655274" @default.
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