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A note on the asymptotic behaviour of empirical likelihood statistics

机译:关于经验似然统计的渐近行为的一个注记

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This paper develops some theoretical results about the asymptotic behaviour of the empirical likelihood and the empirical profile likelihood statistics, which originate from fairly general estimating functions. The results accommodate, within a unified framework, various situations potentially occurring in a wide range of applications. For this reason, they are potentially useful in several contexts, such as, for example, in inference for dependent data. We provide examples showing that known findings in literature about the asymptotic behaviour of some empirical likelihood statistics in time series models can be derived as particular cases of our results.
机译:本文针对经验似然性和经验轮廓似然统计量的渐近行为提出了一些理论结果,这些理论结果来自相当普遍的估计函数。结果在一个统一的框架内适应了可能在多种应用中发生的各种情况。由于这个原因,它们在几种情况下可能很有用,例如,在推断相关数据时。我们提供的例子表明,时间序列模型中一些经验似然统计的渐近行为的文献中的已知发现可以作为我们结果的特殊情况而得出。

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