COLLEGE OF ARTS AND SCIENCES Department of Mathematics and Statistics
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Mathematics Colloquium: Distributed Inference for Massive Data


4:10pm Webster Hall 11

Liuhua Peng

This project studies statistical inference for a general type of statistics in the context of massive data. When the data are stored on multiple platforms, it is usually expensive and slow to do data communication. To deal with this issue, we propose a distributed statistic which can be distributedly computed and hence reduce computing time significantly. We investigate statistical efficiency of the distributed statistics. In addition, we propose two distributed bootstrap algorithms which are computationally effective and consistent theoretically. Applications of our theories and simulation studies are provided to support them. Refreshments served at 3:30 p.m. Hacker Lounge (Neill 216)