Randomness of Shapes and Statistical Inference on Shapes via the Smooth Euler Characteristic Transform
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arXiv
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| Hauptverfasser: | , , , |
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| Format: | Preprint |
| Veröffentlicht: |
2022
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| _version_ | 1866914808323899392 |
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| author | Meng, Kun Wang, Jinyu Crawford, Lorin Eloyan, Ani |
| author_facet | Meng, Kun Wang, Jinyu Crawford, Lorin Eloyan, Ani |
| contents | In this article, we establish the mathematical foundations for modeling the randomness of shapes and conducting statistical inference on shapes using the smooth Euler characteristic transform. Based on these foundations, we propose two chi-squared statistic-based algorithms for testing hypotheses on random shapes. Simulation studies are presented to validate our mathematical derivations and to compare our algorithms with state-of-the-art methods to demonstrate the utility of our proposed framework. As real applications, we analyze a data set of mandibular molars from four genera of primates and show that our algorithms have the power to detect significant shape differences that recapitulate known morphological variation across suborders. Altogether, our discussions bridge the following fields: algebraic and computational topology, probability theory and stochastic processes, Sobolev spaces and functional analysis, analysis of variance for functional data, and geometric morphometrics. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2204_12699 |
| institution | arXiv |
| publishDate | 2022 |
| record_format | arxiv |
| spellingShingle | Randomness of Shapes and Statistical Inference on Shapes via the Smooth Euler Characteristic Transform Meng, Kun Wang, Jinyu Crawford, Lorin Eloyan, Ani Methodology In this article, we establish the mathematical foundations for modeling the randomness of shapes and conducting statistical inference on shapes using the smooth Euler characteristic transform. Based on these foundations, we propose two chi-squared statistic-based algorithms for testing hypotheses on random shapes. Simulation studies are presented to validate our mathematical derivations and to compare our algorithms with state-of-the-art methods to demonstrate the utility of our proposed framework. As real applications, we analyze a data set of mandibular molars from four genera of primates and show that our algorithms have the power to detect significant shape differences that recapitulate known morphological variation across suborders. Altogether, our discussions bridge the following fields: algebraic and computational topology, probability theory and stochastic processes, Sobolev spaces and functional analysis, analysis of variance for functional data, and geometric morphometrics. |
| title | Randomness of Shapes and Statistical Inference on Shapes via the Smooth Euler Characteristic Transform |
| topic | Methodology |
| url | https://arxiv.org/abs/2204.12699 |