Keynote Speakers/主讲嘉宾

张金廷.jpg

Prof. Zhang Jin-Ting, National University of Singapore, Singapore

张金廷教授，新加坡国立大学

Research Area: Functional data analysis; High-dimensional Data Analysis; Nonparametric mixed-effects modeling for longitudinal data; Nonparametric techniques in medical applications

研究领域：功能数据分析；高维数据分析；纵向数据的非参数混合效应建模；医疗应用中的非参数技术

Title：Simple and Adaptive Tests for Big Data

标题：简单, 自适应的大数据测试

Abstract：

In this talk, we consider hypothesis testing problems for big data where the data dimension may be much larger than the sample size so that traditional multivariate tests are no longer applicable. Via imposing strong assumptions on the underlying covariance matrices, most of the existing tests are shown to be asymptotically normal and are conducted using normal approximation. However, in real data analysis, these imposed assumptions may not be satisfied or hardly checked so that these existing tests are less useful. Unlike the existing tests, we proposed simple and adaptive tests. Without imposing strong assumptions on the underlying covariance matrices, the distributions of the proposed tests are shown to be asymptotically chi-squared mixtures and are conducted using chi-square approximation. Good performance of the proposed tests is demonstrated via some simulation studies and real data examples.