ArticleAnnals of statistics2024
MAXIMUM LIKELIHOOD FOR HIGH-NOISE GROUP ORBIT ESTIMATION AND SINGLE-PARTICLE CRYO-EM.
Article in Annals of statistics, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
What it found
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Who cites it
7 citing papers in PubMed.
- Two datasets are better than one: method of double moments for 3D reconstruction in cryo-EM.Inverse problems · 2026Article
- Bayesian perspective for orientation determination in cryo-EM with application to structural heterogeneity analysis.Acta crystallographica. Section D, Structural biology · 2026Article
- Subspace Method of Moments forSIAM journal on imaging sciences · 2026Article
- Bayesian Perspective for Orientation Determination in Cryo-EM with Application to Structural Heterogeneity Analysis.bioRxiv : the preprint server for biology · 2025Article
- A new statistical metric for robust target detection in cryo-EM using 2D template matching.IUCrJ · 2025Article
- Autocorrelation analysis for cryo-EM with sparsity constraints: Improved sample complexity and projection-based algorithms.Proceedings of the National Academy of Sciences of the United States of America · 2023Article
- Toward Single Particle Reconstruction without Particle Picking: Breaking the Detection Limit.SIAM journal on imaging sciences · 2023Article
Corrections and comments
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Authors and funding
5 authors.
Funding
Abstract
Motivated by applications to single-particle cryo-electron microscopy (cryo-EM), we study several problems of function estimation in a high noise regime, where samples are observed after random rotation and possible linear projection of the function domain. We describe a stratification of the Fisher information eigenvalues according to transcendence degrees of graded pieces of the algebra of group invariants, and we relate critical points of the log-likelihood landscape to a sequence of moment optimization problems, extending previous results for a discrete rotation group without projections. We then compute the transcendence degrees and forms of these optimization problems for several examples of function estimation under
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Registered trials
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