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Thomas Severini is a Professor in the Department of Statistics and Data Science at Northwestern University. He obtained his Ph.D. from the University of Chicago in 1987. Professor Severini's research focuses on likelihood-based statistical methods, including maximum likelihood estimation, tests, and confidence regions based on the likelihood ratio statistic. His work is concerned with higher-order asymptotic approximations and the development of statistical methodologies applicable to finance and econometrics. In addition to theoretical aspects, he is interested in the application of statistical methods to analyze sports data. His recent publications include papers on integrated likelihood functions, non-Bayesian inference, and the efficiency of estimating linear functionals in nonparametric regression models. He has published several works in prestigious journals, as well as books on statistical methods in financial models and the application of mathematics in sports statistics.
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