Robust Inference for Dyadic Data with Spatially Dependent Nodes
Abstract
We develop inference for complete dyadic samples with spatial dependence governed by geographic distances between nodes, covering settings beyond the scope of existing dyadic inference theory. We propose spatial variance and corrected block jackknife estimators consistent in nondegenerate and degenerate Gaussian cases. Under degeneracy, spatial subsampling consistently estimates possibly non-Gaussian limits. Combining the jackknife with subsampling yields the max jackknifes-ubsampling (MJS) interval, which provides pointwise asymptotically exact coverage in both Gaussian cases and conservative coverage in the specified non-Gaussian case. Fixed-effect extensions show that estimating node effects can change the fast limiting distribution. Simulations and an empirical illustration are provided.
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BibTeX
@misc{hounyo2026robust,
title = {{Robust Inference for Dyadic Data with Spatially Dependent Nodes}},
author = {Ulrich Hounyo and Jiahao Lin and Xiaojun Song},
howpublished = {arXiv},
year = {2026},
month = oct,
doi = {10.48550/arxiv.2605.28349},
eprint = {2605.28349},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2605.28349},
note = {CC0},
}
RIS
TY - UNPB
TI - Robust Inference for Dyadic Data with Spatially Dependent Nodes
AU - Ulrich Hounyo
AU - Jiahao Lin
AU - Xiaojun Song
PY - 2026
DA - 2026/10/06
PB - arXiv
DO - 10.48550/arxiv.2605.28349
UR - https://arxiv.org/abs/2605.28349
AB - We develop inference for complete dyadic samples with spatial dependence governed by geographic distances between nodes, covering settings beyond the scope of existing dyadic inference theory. We propose spatial variance and corrected block jackknife estimators consistent in nondegenerate and degenerate Gaussian cases. Under degeneracy, spatial subsampling consistently estimates possibly non-Gaussian limits. Combining the jackknife with subsampling yields the max jackknifes-ubsampling (MJS) interval, which provides pointwise asymptotically exact coverage in both Gaussian cases and conservative coverage in the specified non-Gaussian case. Fixed-effect extensions show that estimating node effects can change the fast limiting distribution. Simulations and an empirical illustration are provided.
ER -