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University of Cambridge > Talks.cam > Isaac Newton Institute Seminar Series > Testing covariance matrices in high dimensions
Testing covariance matrices in high dimensionsAdd to your list(s) Download to your calendar using vCal
If you have a question about this talk, please contact INI IT. This talk has been canceled/deleted 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. This talk is part of the Isaac Newton Institute Seminar Series series. This talk is included in these lists:This talk is not included in any other list Note that ex-directory lists are not shown. |
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