Testing covariance matrices in high dimensions
40 mins 36 secs,
147.13 MB,
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About this item
| Description: |
Li, D
Tuesday 27th February 2018 - 11:00 to 12:00 |
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| Created: | 2018-03-01 10:34 |
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| Collection: | Statistical scalability |
| Publisher: | Isaac Newton Institute |
| Copyright: | Li, D |
| Language: | eng (English) |
| Distribution: |
World
(downloadable)
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| Explicit content: | No |
| Aspect Ratio: | 16:9 |
| Screencast: | No |
| Bumper: | UCS Default |
| Trailer: | UCS Default |
| Abstract: | Testing covariance structure is of significant interest in many areas of high-dimensional inference. Using extreme-value form statistics to test against sparse alternatives and using quadratic form statistics to test against dense alternatives are two important testing procedures for high-dimensional independence. However, quadratic form statistics suffer from low power against sparse alternatives, and extreme-value form statistics suffer from low power against dense alternatives with small disturbances. It would be important and appealing to derive powerful testing procedures against general alternatives (either dense or sparse), which is more realistic in real-world applications. Under the ultra high-dimensional setting, we propose two novel testing procedures with explicit limiting distributions to boost the power against general alternatives. |
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