Minimax-optimal estimation for sparse multi-reference alignment with collision-free signals

• URL: http://arxiv.org/abs/2312.07839v1
• Date: Wed, 13 Dec 2023 02:06:52 GMT
• Title: Minimax-optimal estimation for sparse multi-reference alignment with collision-free signals
• Authors: Subhro Ghosh, Soumendu Sundar Mukherjee, Jing Bin Pan
• Abstract summary: We investigate minimax optimality for signal estimation under the MRA model for so-called collision-free signals. We demonstrate that the minimax optimal rate of estimation in for the sparse MRA problem in this setting is $sigma2/sqrtn$, where $n$ is the sample size.
• Score: 1.3658544194443192
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The Multi-Reference Alignment (MRA) problem aims at the recovery of an unknown signal from repeated observations under the latent action of a group of cyclic isometries, in the presence of additive noise of high intensity $\sigma$. It is a more tractable version of the celebrated cryo EM model. In the crucial high noise regime, it is known that its sample complexity scales as $\sigma^6$. Recent investigations have shown that for the practically significant setting of sparse signals, the sample complexity of the maximum likelihood estimator asymptotically scales with the noise level as $\sigma^4$. In this work, we investigate minimax optimality for signal estimation under the MRA model for so-called collision-free signals. In particular, this signal class covers the setting of generic signals of dilute sparsity (wherein the support size $s=O(L^{1/3})$, where $L$ is the ambient dimension. We demonstrate that the minimax optimal rate of estimation in for the sparse MRA problem in this setting is $\sigma^2/\sqrt{n}$, where $n$ is the sample size. In particular, this widely generalizes the sample complexity asymptotics for the restricted MLE in this setting, establishing it as the statistically optimal estimator. Finally, we demonstrate a concentration inequality for the restricted MLE on its deviations from the ground truth.

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