Determining the signal dimension in second order source separation
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arXiv
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| Format: | Preprint |
| Published: |
2018
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| _version_ | 1866910405486444544 |
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| author | Virta, Joni Nordhausen, Klaus |
| author_facet | Virta, Joni Nordhausen, Klaus |
| contents | While an important topic in practice, the estimation of the number of non-noise components in blind source separation has received little attention in the literature. Recently, two bootstrap-based techniques for estimating the dimension were proposed, and although very efficient, they suffer from the long computation times caused by the resampling. We approach the problem from a large sample viewpoint and develop an asymptotic test for the true dimension. Our test statistic based on second-order temporal information has a very simple limiting distribution under the null hypothesis and requires no parameters to estimate. Comparisons to the resampling-based estimates show that the asymptotic test provides comparable error rates with significantly faster computation time. An application to sound recording data is used to illustrate the method in practice. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_1808_10669 |
| institution | arXiv |
| publishDate | 2018 |
| record_format | arxiv |
| spellingShingle | Determining the signal dimension in second order source separation Virta, Joni Nordhausen, Klaus Statistics Theory While an important topic in practice, the estimation of the number of non-noise components in blind source separation has received little attention in the literature. Recently, two bootstrap-based techniques for estimating the dimension were proposed, and although very efficient, they suffer from the long computation times caused by the resampling. We approach the problem from a large sample viewpoint and develop an asymptotic test for the true dimension. Our test statistic based on second-order temporal information has a very simple limiting distribution under the null hypothesis and requires no parameters to estimate. Comparisons to the resampling-based estimates show that the asymptotic test provides comparable error rates with significantly faster computation time. An application to sound recording data is used to illustrate the method in practice. |
| title | Determining the signal dimension in second order source separation |
| topic | Statistics Theory |
| url | https://arxiv.org/abs/1808.10669 |