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| Main Author: | |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2508.02678 |
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Table of Contents:
- This paper studies the family of sliced Cramér metrics, quantifying their stability under distortions of the input functions. Our results bound the growth of the sliced Cramér distance between a function and its geometric deformation by the product of the deformation's displacement size and the function's mean mixed norm. These results extend to sliced Cramér distances between tomographic projections. In addition, we remark on the effect of convolution on the sliced Cramér metrics. We also analyze efficient Fourier-based discretizations in 1D and 2D, and prove that they are robust to heteroscedastic noise. The results are illustrated by numerical experiments.