The generalized method of moments for multi-reference alignment
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Abstract (arXiv)
This paper studies the application of the generalized method of moments (GMM) to multi-reference alignment (MRA): the problem of estimating a signal from its circularly-translated and noisy copies. We begin by proving that the GMM estimator maintains its asymptotic optimality for statistical models with group symmetry, including MRA. Then, we conduct a comprehensive numerical study and show that the GMM substantially outperforms the classical method of moments, whose application to MRA has been studied thoroughly in the literature. We also formulate the GMM to estimate a three-dimensional molecular structure using cryo-electron microscopy and present numerical results on simulated data.
Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). First posted on arXiv 3 Mar 2021.
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Citing papers per year: 2021: 1; 2022: 1; 2023: 0; 2024: 0; 2025: 2.
4 papers.
- 2021An accelerated expectation-maximization algorithm for multi-reference alignmentcited by 1
- 2022Unrolled algorithms for group synchronizationcited by 1
- 2025Diagonally-Weighted Generalized Method of Moments Estimation for Gaussian Mixture Modelingcited by 1
- 2025If you can distinguish, you can express: Galois theory, Stone--Weierstrass, machine learning, and linguisticscited by 0
- 2025If you can distinguish, you can express: Galois theory, Stone--Weierstrass, machine learning, and linguistics10 Oct 2025
- 2025Diagonally-Weighted Generalized Method of Moments Estimation for Gaussian Mixture Modeling28 Jul 2025
- 2022Unrolled algorithms for group synchronization19 Jul 2022
- 2021An accelerated expectation-maximization algorithm for multi-reference alignment16 May 2021