prof. dr. John Einmahl

Expertise

John H.J. Einmahl is Arie Kapteyn professor of Statistics at the Department of Econometrics & OR and research fellow at CentER, both at Tilburg University. John's research has been published in leading journals in Statistics and Probability Theory, like the Annals of Statistics, the Annals of Probability, JRSS B, JASA, J. of Econometrics, and Probability Theory and Related Fields. His research interests are mainly in the area of nonparametric statistics and its ramifications, including statistics of extremes, empirical likelihood, generalized and multivariate quantiles, and (local) empirical processes.

John is a fellow of the Institute of Mathematical Statistics and an Associate Editor of Extremes, Econometrics and Statistics, and Statistica Neerlandica. He has been an Associate Editor of The Annals of Statistics, The Annals of Probability, and Bernoulli. He visited Florida State University as a Senior Fulbright Scholar. 

CV John Einmahl

Courses

• Non- and Semiparametric Econometrics (CentER)
• Statistics for Econometrics
• Data Analytics for Non-Life Insurance

Recent publications

1. Two-sample testing for tail copulas with an application to equity ind…

   Can, S. U., Einmahl, J., & Laeven, R. J. A. (2023). Two-sample testing for tail copulas with an application to equity indices. Journal of Business & Economic Statistics. ,

2. Empirical Likelihood Based Testing for Multivariate Regular Variation

   Einmahl, J., & Krajina, A. (2023). Empirical Likelihood Based Testing for Multivariate Regular Variation. (CentER Discussion Paper; Vol. 2023-001). CentER, Center for Economic Research.

3. Extreme value estimation for heterogeneous data

   Einmahl, J., & He, Y. (2023). Extreme value estimation for heterogeneous data. Journal of Business & Economic Statistics, 41(1), 255-269.

4. Cube root weak convergence of empirical estimators of a density level…

   Berthet, P., & Einmahl, J. (2022). Cube root weak convergence of empirical estimators of a density level set. The Annals of Statistics, 50(3), 1423-1446.

5. Extreme Value Inference for General Heterogeneous Data

   Einmahl, J., & He, Y. (2022). Extreme Value Inference for General Heterogeneous Data. (pp. 1-26). (CentER Discussion Paper; Vol. 2022-017). CentER, Center for Economic Research. https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4184764