A bagging-based estimator for dyadic networks with fixed effects attains asymptotic normality and the Cramér-Rao bound for both TU and NTU links by using joint MOM, Le Cam refinement, and split-network jackknife.
Normal approximation in large network models
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Penalized likelihood resolves non-existence of MLE and incidental-parameter bias in network models with degree heterogeneity while allowing sparse networks and providing asymptotic guarantees.
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Bagging the Network
A bagging-based estimator for dyadic networks with fixed effects attains asymptotic normality and the Cramér-Rao bound for both TU and NTU links by using joint MOM, Le Cam refinement, and split-network jackknife.