Yongdao Zhou (周永道) is currently a professor and doctoral supervisor at the School of Statistics and Data Science, Nankai University. He has been selected for several talent projects at national level, Tianjin Municipality and Nankai University. His research interests are experimental design and data mining. He has presided over 4 National Natural Science Foundation projects, 1 key project subproject of National Natural Science Foundation projects, 1 key project of Tianjin Natural Science Foundation and more than 10 other projects. He has visited 5 overseas universities including UCLA. He has published more than 60 academic papers in top journals of statistics such as JRSSB, JASA, Biometrika and Chinese Science. He has co-published three Chinese and English monographs and two statistics textbooks. He won the first prize of the National Statistical Science Research Outstanding Achievement Award and the third prize of the National Statistical Science and Technology Progress Award. 

Title: Model-free global likelihood subsampling for massive data

Abstract: Most existing studies for subsampling heavily depend on a specified model. If the assumed model is not correct, the performance of the subsample may be poor. This paper focuses on a model-free subsampling method, called global likelihood subsampling, such that the subsample is robust to different model choices. It leverages the idea of the global likelihood sampler, which is an effective and robust sampling method from a given continuous distribution. Furthermore, we accelerate the algorithm for largescale datasets and extend it to deal with high-dimensional data with relatively low computational complexity. Simulations and real data studies are conducted to apply the proposed method to regression and classification problems. It illustrates that this method is robust against different modeling methods and has promising performance compared with some existing model-free subsampling methods for data compression.