Eva Cantoni is Full Professor at the Geneva School of Economics and Management of the University of Geneva. She is an Accredited European Statistician by (FENStat).
Her interests are on robust statistics, variable and model selection, nonparametric regression and models for data with excess of zeros. She collaborates with scientists in different areas, and is particularly interested in applications in medicine and in ecology.
She has been the director of the master program in statistics from 2012 to 2019 and of the complementary certificate in applied statistics from 2015 to 2019. She has served as Vice-Dean for Teaching and director of the bachelor program in economics and management between August 2020 and January 2023.
Since July 1st 2024, she is Specialty Chief Editor for the Statistics and Probability section of the Journal Frontiers in Applied Mathematics and Statistics.
For the term 2024-2027, she is also the President of the Swiss Federal Statistics Committee, an advisory body of the Swiss Federal Council.
New publication (February 2025): F. Mason, E. Cantoni and P. Ghisletta . Linear mixed models and latent growth curve models for group comparison studies contaminated by outliers. Psychological Methods. 30(1), 155–173 https://doi.org/10.1037/met0000643
New editorial (July 2024): E. Cantoni, M. Hubert, D. La Vecchia, S. Van Aelst Editorial: Special Issue on Robustness Dedicated to Elvezio Ronchetti and Peter Rousseeuw, https://doi.org/10.1016/j.ecosta.2024.07.001
New arXiv preprint (April 2024): F. Mason, M. Koller, E. Cantoni and P. Ghisletta confintROB Package: Confindence Intervals in robust linear mixed models, https://arxiv.org/abs/2404.08426.
New R package (April 2024) confintROB: Bootstrap Confidence Intervals for Robust and Classical Linear Mixed Model Estimators
New publication (January 2024) E. Cantoni, N. Jacot and P. Ghisletta Review and comparison of measures of explained variation and model selection in linear mixed-effects models, 29 , 150-168 Econometrics and Statistics. https://doi.org/10.1016/j.ecosta.2021.05.005 (GitHub site).
Peer reviewed papers - statistical methodology
Peer reviewed papers - statistical applications
Editorials