Statistical Aggregation in Big Data

Speaker:Lin Nan,Associate Professor,Washington University

Title:Statistical Aggregation in Big Data

Date&Time: June 30th, 2015,330pm---430pm

Location:Lecture Hall, Floor 4th Mathematics and Physics Building


Big data problems present great challenges to statistical analyses, especially from the computational side. We consider a wide range of statistical inference problems in big data problems. The statistical aggregation strategy is a divide-and-conquer approach that aims to achieve asymptotic equivalence. In addition to solve memory and storage difficulties appeared in big data, it may also provide a computational efficient strategy in a non-big data context.  Through both theoretical proof and simulations, we show that our method significantly reduces the computational time and meanwhile maintains the asymptotic efficiency.

About the speaker

Nan Lin is an Associate Professor in the Department of Mathematics at Washington University in St. Louis and has a joint appointment in the Division of Biostatistics, Washington University in St. Louis, School of Medicine.  His methodological research is in the areas of statistical computing for massive data, Bayesian regularization, bioinformatics, longitudinal and functional data analysis and psychometrics. His applied research involves statistical analysis of data from anesthesiology, genomics and cognition. He earned a B.S. (1999) from University of Science and Technology of China, a M.S. (2000) and Ph.D. (2003) in Statistics, and a second M.S. (2003) in Finance from University of Illinois at Urbana-Champaign.  Before joining Washington University, he was a postdoctoral associate (2003-2004) at the Center for Statistical Genomics and Proteomics, Yale University.