Sufficient conditions are given for pseudo-likelihood estimation of both parameters in the Potts model at rate sqrt(N) for bounded-degree or irregular graphs, with impossibility shown for certain dense regular graphs, plus a new concentration inequality via nonlinear large deviations.
Exponential random graph model parameter estimation for very large directed networks
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Joint Estimation in Potts Model
Sufficient conditions are given for pseudo-likelihood estimation of both parameters in the Potts model at rate sqrt(N) for bounded-degree or irregular graphs, with impossibility shown for certain dense regular graphs, plus a new concentration inequality via nonlinear large deviations.