Asymptotic testing of covariance separability for matrix elliptical data
Fuente:
arXiv
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| Autori principali: | , |
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| Natura: | Preprint |
| Pubblicazione: |
2026
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| _version_ | 1866917219544334336 |
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| author | Virta, Joni Matsuda, Takeru |
| author_facet | Virta, Joni Matsuda, Takeru |
| contents | We propose a new asymptotic test for the separability of a covariance matrix. The null distribution is valid in wide matrix elliptical model that includes, in particular, both matrix Gaussian and matrix $t$-distribution. The test is fast to compute and makes no assumptions about the component covariance matrices. An alternative, Wald-type version of the test is also proposed. Our simulations reveal that both versions of the test have good power even for heavier-tailed distributions and can compete with the Gaussian likelihood ratio test in the case of normal data. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2601_16684 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Asymptotic testing of covariance separability for matrix elliptical data Virta, Joni Matsuda, Takeru Statistics Theory Methodology We propose a new asymptotic test for the separability of a covariance matrix. The null distribution is valid in wide matrix elliptical model that includes, in particular, both matrix Gaussian and matrix $t$-distribution. The test is fast to compute and makes no assumptions about the component covariance matrices. An alternative, Wald-type version of the test is also proposed. Our simulations reveal that both versions of the test have good power even for heavier-tailed distributions and can compete with the Gaussian likelihood ratio test in the case of normal data. |
| title | Asymptotic testing of covariance separability for matrix elliptical data |
| topic | Statistics Theory Methodology |
| url | https://arxiv.org/abs/2601.16684 |