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.

翻译：本文研究了dyadic数据内核密度估计(KDE)的无症状特性和替代推断方法。 我们首先为 dyadic KDE 设定了统一的趋同率。 其次, 我们提出了修改的 jknife 实验概率程序来推断。 提议的测试统计无论dydic 群集存在与否, 都具有暂时性关键作用。 测试结果进一步扩展, 以涵盖不完全的dydic数据这一实际相关案例。 模拟结果表明, 这一修改过的 jknife 实验性概率推论程序提供了精确的覆盖概率, 即使样本大小较小, 并且有不完整的 dyadic 数据。 最后, 我们通过研究美国的机场拥挤状况来说明这种方法。