Empirical Likelihood and Uniform Convergence Rates for Dyadic Kernel Density Estimation

Bibliographic Data

ID19419237
AuthorsHarold D Chiang (0000-0002-3545-4710, University of Wisconsin–Madison, corresponding author), Bing Yang Tan (0000-0001-8621-5923, Global Asia Institute, National University of Singapore, Singapore, Singapore)
Year2023
Volume41
Issue3
Pages906-914
Publication date2023-07-03
Open AccessNo
TypeARTICLE
VenueJournal of Business and Economic Statistics (JOURNAL)
Journal identifiersISSN: 0735-0015 • E-ISSN: 1537-2707
PublisherInforma UK Limited (PUBLISHER • GB)
DOI10.1080/07350015.2022.2080684
OpenAlexW3093173857
LanguageEN
Citations received1
References cited44

This article 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. Second, 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

Cluster analysis · Confidence interval · Density estimation · Econometrics · Empirical likelihood · Inference · Jackknife resampling · Kernel density estimation · Rate of convergence · Resampling · Statistic · Statistics · Computer Science · Mathematics · Spatial and Panel Data Analysis · Statistical Methods and Bayesian Inference · Statistical Methods and Inference · Artificial Intelligence

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