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Approximations of empirical probability generating processes

机译:经验概率生成过程的近似

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摘要

First we polish an argument of Remillard and Theodorescu for the weak convergence of the empirical probability generating process. Then we prove a general inequality between probability generating processes and the corresponding empirical processes, which readily implies a rate of convergence and trivializes the problem of weak convergence: whenever the empirical process or its non-parametric bootstrap version, or the parametric estimated empirical process or its bootstrap version converges, so does the corresponding probability generating process. Derivatives of the generating process are also considered.
机译:首先,我们完善了Remillard和Theodorescu关于经验概率生成过程的弱收敛的论点。然后,我们证明了概率生成过程与相应的经验过程之间的一般不等式,这很容易暗示收敛速度,并使弱收敛问题变得微不足道:每当经验过程或其非参数引导程序版本,参数估计的经验过程或它的引导程序版本收敛,相应的概率生成过程也收敛。还考虑了生成过程的衍生物。

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