Jianqing Fan is Frederick L. Moore'18 Professor of Finance  and president of the Institute of Mathematical Statistic. 

He is the Co-editor of Econometrical Journal published by Royal Economics Society
and an associate editor of  The Journal of American Statistical Association and was the co-editor of  The Annals of Statitics (2004-2006) and an editor of  Probability Theory and Related Fields (2003-2005).

After receiving his Ph.D. in Statistics from the University of California at Berkeley, he has been appointed as assistant, associate, and full professor at the University of North Carolina at Chapel Hill (1989-2003), and as professor at the University of California at Los Angeles(1997-2000),  Professor of Statistics and Chairman at the Chinese University of Hong Kong (2000-2003), and as professor at the Princeton University(2003--).

He has coauthored two popular books on ``Local Polynomial Modeling'' (1996) and ``Nonlinear time series: Parametric and Nonparametric Methods'' (2003) and authored or coauthored over 100 articles on computational biology, financial econometrics, semi-parametric 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 (6 times). His published work on statistics, financial econometrics, and computational biology has been recognized by The 2000 COPSS Presidents' Award, given annually to an outstanding statistician under age 40 worldwide, Humboldt Research Award for lifetime achievement in 2006, Morngside Gold Medal of Applied Mathematics in 2007, honoring triennially an outstanding applied mathematician of Chinese decent worldwide, and the election to the follow of American Associations for Advancement of Science, Institute of Mathematical Statistics, and American Statistical Assocation.

His research on statistics theory and methods has been funded by various federal agencies, including NSF, NIH and NSA. He has been frequently invited to various professional conferences and workshops and played various leadership roles in statistical community.

Abstract

Nonparametric estimation of genewise variance for Microarray Data

Estimation of genewise variance arises from two important applications of microarrays analysis: Selecting differentially expressed genes and testing whether a microarray data has been properly normalized. A semiparametric model is introduced for estimating genewise variance, which involves vast nuisance parameters.  The problem itself poses significant challenges because the number of nuisance parameters is proportional to the sample size. In this study, we proposed a novel nonparametric estimator using within-array replications and estimated its asymptotical properties. The rates of convergence is established and demonstrated by simulation studies. The estimator also improves the power of the test of detecting statistically differentially expressed genes. The methodology is illustrated by the microarry data from MicroArray Quality Control (MAQC) project.

(with Yang Feng and Yue Niu)