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Estimating and Testing Covariance Matrices in High Dimensions
澳门新葡8455最新网站:2018年01月17日 16:15 点击数:

报告人:李丹宁

报告地点:澳门新葡8455最新网站403室

报告澳门新葡8455最新网站:2018年01月20日星期六14:00-15:00

邀请人:

报告摘要:

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.

主讲人概况:

李丹宁,吉林大学数学新葡京最新官网讲师,美国明尼苏达大学博士,主要从事高维协方差矩阵的研究。

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