论文标题
二元核密度估计的经验可能性和均匀收敛速率
Empirical likelihood and uniform convergence rates for dyadic kernel density estimation
论文作者
论文摘要
本文研究了二元数据的核密度估计（KDE）的渐近特性和替代推理方法。我们首先为二元KDE建立均匀的收敛速率。其次，我们提出了一种修改的折刀经验可能性的推理程序。所提出的测试统计量渐近关键，而不论存在二元聚类。结果将进一步扩展，以涵盖不完整的二元数据的实际相关情况。仿真表明，这种改良的小刀经验可能性推理过程即使使用适度的样本量和不完整的二元数据也提供精确的覆盖概率。最后，我们通过研究美国的机场拥塞来说明这种方法。
This paper studies the asymptotic properties of and alternative inference methods for kernel density estimation (KDE) for dyadic data. We first establish uniform convergence rates for dyadic KDE. Secondly, we propose a modified jackknife empirical likelihood procedure for inference. The proposed test statistic is asymptotically pivotal regardless of presence of dyadic clustering. The results are further extended to cover the practically relevant case of incomplete dyadic data. Simulations show that this modified jackknife empirical likelihood-based inference procedure delivers precise coverage probabilities even with modest sample sizes and with incomplete dyadic data. Finally, we illustrate the method by studying airport congestion in the United States.
