Thursday, Mar 30, 2017 - 3:30pm - Allen 14
Murray/Ollivier Lecture
Challenges on analysis of big data
Dr. Jianqing Fan, Statistics, Princeton University

Title:  Challenges on analysis of big data
Abstract:  Big Data arise from almost all aspects of human endeavors, from frontiers of scientific research to societal developments. They hold great promise for the discovery of heterogeneity and the search for personalized treatments. They also allow us to find weak patterns in presence of large individual variations. Salient features of Big Data include heterogeneity, noise accumulation, spurious correlations, incidental endogeneity, measurement errors, computational cost, data storage, retrieval, and communciations. These have huge impact on the system and analysis and should be seriously considered in the development of statistical procedures. We will address several of these issues in this talk from distributed inference and robust analysis and illustrate the importance of robustness by using financial and economic data

This talk should be accessible to undergraduates.

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