Title: High Dimensional (Mean) Independence Tests Based on Rank Indices in the Presence of Heterogeneity
Speaker: Liping Zhu（Institute of Statistics and Big Data Renmin University of China）
Time: Friday, April 21, 10:00-11:00
Location:201 Mingde Building (Zone B)
Abstract：In the big data era, how to deal with heterogeneous observations is an inevitable and important issue. We consider testing (mean) independence in the presence of heterogeneity. To be precise, in the first part of the talk, we consider testing for the effects of high-dimensional covariates on the response. In the second part of the talk, we propose three tests to test independence between two high-dimensional random vectors based on the rank-based indices
To simultaneously accommodate such heteroscedasticity and high dimensionality, we propose novel tests based on aggregations, requiring no prior information on the specific form of models. Our proposed test statistics are scale-invariance, tuning-free and convenient to implement. We further study the asymptotic relative efficiency of our proposed test with respect to the state-of-art universal tests.
