Develops functional multi-target detection theory and recovery algorithms via bispectrum inversion with non-asymptotic guarantees for compactly supported signals under continuous translations and correlated Gaussian noise.
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2026 2verdicts
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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.
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Functional Multi-Target Detection via Bispectrum Inversion
Develops functional multi-target detection theory and recovery algorithms via bispectrum inversion with non-asymptotic guarantees for compactly supported signals under continuous translations and correlated Gaussian noise.
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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.