Prof Jianqing Fan from Princeton University discusses the salient features of Big Data which include experimental variations, computational cost, noise accumulation, spurious correlations, incidental endogeneity, and measurement errors, and how these issues influence the Big Data analysis and the development of statistical procedures.
Free and open to the public. Seating is on a first-come first-served basis.
Big Data arise from many frontiers of scientific research and technological developments. They hold great promise for the discovery of heterogeneity and the search for personalized treatments. They also allow people to find weak patterns in presence of large individual variations. Salient features of Big Data include experimental variations, computational cost, noise accumulation, spurious correlations, incidental endogeneity, and measurement errors. These issues should be seriously considered in Big Data analysis and in the development of statistical procedures. As an example, the speaker and his research group offered here the sparest solution in high-confidence sets as a generic solution to high-dimensional statistical inference and they derived a useful mean-square error bound. This method combines naturally two pieces of useful information: the data and the sparsity assumption.
About the speaker
Prof Jianqing Fan received his PhD in Statistics from the University of California at Berkeley in 1989. He has been appointed as Assistant, Associate, and Full professor at the University of North Carolina at Chapel Hill from 1989 to 2003, Professor at the University of California at Los Angeles from 1997 to 2000, Professor of Statistics and Chairman at the Chinese University of Hong Kong from 2000 to 2003. He joined Princeton University in 2003, where he is currently Frederick L. Moore '18 Professor of Finance, Professor of Statistics, and Chairman of Department of Operations Research and Financial Engineering.
Prof Fan’s research interests are in statistical methods in finance, economics, risk management, high-dimensional statistics and machine learning computational biology, biostatistics, nonparametric modeling, longitudinal and functional data analysis, nonlinear time series, wavelets and their applications, among others. He has coauthored two highly-regarded books on Local Polynomial Modeling and Nonlinear Time Series: Parametric and Nonparametric Methods and authored or coauthored over 170 articles on finance, economics, computational biology, semiparametric and non-parametric modeling, statistical learning, nonlinear time series, survival analysis, longitudinal data analysis, and other aspects of theoretical and methodological statistics. He has been consistently ranked as a top 10 highly-cited mathematical scientist since the existence of such a ranking.
Prof Fan received numerous prestigious awards including the Presidents' Award given by the Committee of Presidents of Statistical Societies, the Humboldt Research Award for lifetime achievement and the Morningside Gold Medal of Applied Mathematics. He was an invited speaker at the 2006 International Congress for Mathematicians. He is a Fellow of the American Association for the Advancement of Science, the Institute of Mathematical Statistics, and the American Statistical Association. He was elected to Academician from Academia Sinica in 2012.
Free and open to the public. Seating is on a first-come first-served basis.
