学术报告
An Extreme-Value Approach for Testing the Equality of Large U-StatisticBased Correlation Matrices - 张新生（复旦大学）
题目：An Extreme-Value Approach for Testing the Equality of Large U-StatisticBased Correlation Matrices
报告人：张新生（复旦大学）
Abstract
There has been an increasing interest in testing the equality of large Pearson’s correlation matrices. However, in many applications it is more important to test the equality of large rank-based correlation matrices since they are more robust to outliers and nonlinearity. Unlike the Pearson’s case, testing the equality of large rank-based statistics has not been well explored. In this talk, we provide a framework for testing the equality of two large U-statistic based correlation matrices, which include the rank-based correlation matrices as special cases. Our approach exploits extreme value statistics and the Jackknife estimator for uncertainty assessment and is valid under a fully nonparametric model. Theoretically, we develop a theory for testing the equality of U-statistic based correlation matrices. We then apply this theory to study the problem of testing large Kendall’s tau correlation matrices and demonstrate its optimality.
时间： 11月28日（周二）上午9:45-10:30
地点：首都师范大学本部教二楼913教室
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