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Thomas Severini

Professor of Statistics and Data Science

Ph.D., 1987, University of Chicago

Research Interests

My research focuses on likelihood-based statistical methods such as maximum likelihood estimation and tests and confidence regions based on the likelihood ratio statistic.  My work in this area is concerned with higher-order asymptotic approximations to the distributions of likelihood-based statistics and the development of statistical methodology for models with many parameters, including applications in finance and econometrics.  I am also interested in the application of statistical methods to the analysis of sports data.

Recent Publications

  • Integrated Likelihood Functions for Non-Bayesian Inference, Biometrika, 94 (2007),  pp. 529-542.
  • Likelihood Ratio Statistics Based on an Integrated Likelihood, Biometrika, 97 (2010),  pp. 481-96.
  • Frequency Properties of Inferences Based on an Integrated Likelihood Function, Statistica Sinica 21 (2011), pp. 433-47.
  • Efficiency bounds for estimating linear functionals of nonparametric regression models with endogenous regressors (with G. Tripathi),  Journal of Econometrics, 170 (2012), pp. 491-8.
  • A Flexible Approach to Inference in Semiparametric Regression Models with Correlated Errors using Gaussian Processes (with H. He),  Computational Statistics and Data Analysis, 103 (2016), 316-29.
  • A Nonparametric Approach to Measuring the Sensitivity of an Asset's Return to the Market,  Annals of Finance, 12 (2016), 179-99.
  • How jet lag impairs major league baseball performance (with A. Song and R. Allada), Proc. Nat. Acad. Science,  114 (2017), 1407-1412.
  • Analytic Methods in Sports: Using Mathematics and Statistics to Understand Data from Baseball, Football, Basketball, and Other Sports, (2014),   published by CRC Press.
  • Introduction to Statistical Methods for Financial Models, (2017), published by CRC Press.