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| Autori principali: | , |
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| Natura: | Preprint |
| Pubblicazione: |
2022
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| Soggetti: | |
| Accesso online: | https://arxiv.org/abs/2209.02025 |
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| _version_ | 1866913571333472256 |
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| author | Rabenoro, Dimbihery Pennec, Xavier |
| author_facet | Rabenoro, Dimbihery Pennec, Xavier |
| contents | In this article, we develop an asymptotic method for constructing confidence regions for the set of all linear subspaces arising from PCA, from which we derive hypothesis tests on this set. Our method is based on the geometry of Riemannian manifolds with which some sets of linear subspaces are endowed. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2209_02025 |
| institution | arXiv |
| publishDate | 2022 |
| record_format | arxiv |
| spellingShingle | A geometric framework for asymptotic inference of principal subspaces in PCA Rabenoro, Dimbihery Pennec, Xavier Statistics Theory 62R30, 60F05 In this article, we develop an asymptotic method for constructing confidence regions for the set of all linear subspaces arising from PCA, from which we derive hypothesis tests on this set. Our method is based on the geometry of Riemannian manifolds with which some sets of linear subspaces are endowed. |
| title | A geometric framework for asymptotic inference of principal subspaces in PCA |
| topic | Statistics Theory 62R30, 60F05 |
| url | https://arxiv.org/abs/2209.02025 |