A projection posterior for exponentially tilted empirical likelihood that integrates generative AI auxiliary data, with new Bernstein-von Mises and consistency theorems under vanishing and persistent prior regimes.
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2026 2verdicts
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QBHM estimator matches standard GMM asymptotics for strongly identified parameters and is a Bayes rule under squared loss in weak-GMM limit experiments induced by the hierarchy.
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Empirical Likelihood with Generative AI
A projection posterior for exponentially tilted empirical likelihood that integrates generative AI auxiliary data, with new Bernstein-von Mises and consistency theorems under vanishing and persistent prior regimes.
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Quasi-Bayesian Hierarchical Models
QBHM estimator matches standard GMM asymptotics for strongly identified parameters and is a Bayes rule under squared loss in weak-GMM limit experiments induced by the hierarchy.