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Statistics is a discipline that studies designs, collects and analyzes data from all scientific disciplinary. It pervades every facets of quantitative analysis in science and engineering. The aims of statistics include understanding sampling variability, making inferences and decisions from noisy data, establishing relationship between the covariates and response variables, and predicting future events. The main focus of statistical research at the Department of Operations Research and Financial Engineering is to study the problems arising from Financial Engineering, Security Pricing, Bioinformatics and Health Sciences.

The statistics laboratory is part of the Department of Operations Research and Financial Engineering at Princeton University and is led by Professor Jianqing Fan . Fan's group is interested in statistical methods in financial econometrics and risk managements, computational biology, biostatistics, high-dimensional statistical learning, data-analytic modeling, longitudinal and functional data analysis, nonlinear time series, wavelets and their applications, statistical learning, nonparametric statistics among others. Our primary research focuses on developing and justifying statistical methods that are used to solve problems at the frontier of scientific research. This is expanded into other disciplines where the statistics discipline is useful.

In each of the areas mentioned above, our group devotes most of our efforts to the search for intuitively appealing, model-free, robust nonparametric approaches and illustrates the approaches by real data and simulated examples. Modern statistical principles and modeling inevitably involve intensive computation, which is a part of the methodological research development. Our group is also very interested in developing foundational statistical theory and in providing fundamental insights to sophisticated statistical models. These include sampling theory, statistical learning theory, minimax theory, efficient semi-parametric modeling and nonlinear function estimation.

Recently, our group is particularly interested in financial econometrics, risk management, computational biology, biostatistics, high-dimensional data-analytic modeling and inferences, nonlinear time series, analysis of longitudinal and functional data, and other interdisciplinary collaborations.

According to Science Watch, a weekly web publication by Thomson Reuters, Princeton University is a "high-impact" research institution in the areas of probability and statistics. Princeton claims the third spot among institutions ranked by the number of citations per paper. The comparison data was drawn from U.S.-based institutions, specifically those with at least 75 papers appearing in journals indexed by Thomas Reuters between 2005 and 2009. The rankings appear in this Sci-Bytes entry for the week of January 2nd, which cites InCites™ Global Comparisons as well as Thomson Reuters' publication index.

Research topics

  • Statistics Theory and Methods
  • Data-analytic modeling
  • Nonlinear time series
  • Data-analytic modeling
  • Analysis of longitudinal data
  • Model selection
  • Survival Analysis
  • High-dimensional inference