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.
Fixed-effect regressions on network data
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Penalized Likelihood for Dyadic Network Formation Models with Degree Heterogeneity
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.