In low-SNR Gaussian latent-variable models, optimally weighted GMoM using minimal-order moments achieves the same leading asymptotic covariance as MLE via matching layerwise expansions of the information operators.
Orbit recovery under the rigid motions group.arXiv preprint arXiv:2512.07405, 2025
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The generalized method of moments is (almost) statistically efficient in low-SNR Gaussian latent-variable models
In low-SNR Gaussian latent-variable models, optimally weighted GMoM using minimal-order moments achieves the same leading asymptotic covariance as MLE via matching layerwise expansions of the information operators.