• Corpus ID: 246009632

Uniform Inference for Kernel Density Estimators with Dyadic Data

@inproceedings{Cattaneo2022UniformIF,
  title={Uniform Inference for Kernel Density Estimators with Dyadic Data},
  author={M. D. Cattaneo and Yingjie Feng and W. Underwood},
  year={2022}
}
Dyadic data is often encountered when quantities of interest are associated with the edges of a network. As such it plays an important role in statistics, econometrics and many other data science disciplines. We consider the problem of uniformly estimating a dyadic Lebesgue density function, focusing on nonparametric kernel-based estimators which take the form of U-process-like dyadic empirical processes. We provide uniform point estimation and distributional results for the dyadic kernel… 

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