# 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…

## 3 Citations

### Empirical likelihood and uniform convergence rates for dyadic kernel density estimation

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This paper studies the asymptotic properties of and improved inference methods for kernel density estimation (KDE) for dyadic data. We first establish novel uniform convergence rates for dyadic KDE…

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This paper presents novel methods and theories for estimation and inference about parameters in econometric models using machine learning for nuisance parameters estimation when data are dyadic. We…

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