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Some New Insights in High Dimensional Independence Tests
澳门新葡8455最新网站:2016年11月30日 14:23 点击数:

报告人:李丹宁

报告地点:澳门新葡8455最新网站104室

报告澳门新葡8455最新网站:2016年12月01日星期四15:00-16:00

邀请人:

报告摘要:

In this talk, we will discuss some new insights in the independence tests of high-dimensional data. In the current literature, two sets of test statistics are commonly used for various high-dimensional tests:1) using extreme-value form statistics to test against sparse alternatives, and 2) using quadratic form statistics to test against dense alternatives. 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 and may have size distortions due to its slow convergence. For real-world applications, it is important to derive powerful testing procedures against more general alternatives. We introduce new testing procedures via distance correlations to boost the power against more general alternatives and retain the correct asymptotic size. Under the high-dimensional setting, we derive the closed-form limiting null distributions, and obtain their explicit rates of uniform convergence. This talk is based on a joint work with Lingzhou Xue at Penn State and Hui Zou at University of Minnesota.

主讲人概况:

美国明尼苏达大学统计系博士,现为吉林大学统计系教师。

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