Establishes local asymptotic minimax optimality and limit of experiments for estimating the natural parameter of Ising models on inhomogeneous random graphs via a computationally efficient one-step estimator.
Joint parameter estimations for spin glasses
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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.
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Ising Models on Inhomogeneous Random Graphs: Inference, Local Asymptotic Minimaxity, and Limit of Experiments
Establishes local asymptotic minimax optimality and limit of experiments for estimating the natural parameter of Ising models on inhomogeneous random graphs via a computationally efficient one-step estimator.
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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.