Christophe Ley is Assistant Professor of Mathematical Statistics at the Department of Applied Mathematics, Computer Science and Statistics. His main research topics are flexible modelling, efficient inference, Stein’s Method in Machine Learning and Statistics, and sport analytics. He is Associate Editor of three international journals, has written one book and edited a second book, and (co-)authored more than 50 research papers. He has been teaching machine learning and data mining topics in the course “Big Data Science” in the Advanced Master of Statistical Data Analysis.
Keywords: Directional statistics, Distribution theory, Semi-parametric statistics, Sport analytics, Statistical inference, Stein’s Method
Groll, A., Ley, C., Schauberger, G. and Van Eetvelde, H. (2019) A hybrid random forest to predict soccer matches in international tournaments. Journal of Quantitative Analysis in Sports, in press.
Ley, C., Reinert, G. and Swan, Y. (2017) Distances between nested densities and a measure of the impact of the prior in Bayesian statistics. Annals of Applied Probability 27, 216-241.
Ley, C., Reinert, G. and Swan, Y. (2017) Stein’s method for comparison of univariate distributions. Probability Surveys 14, 1-52.
Ley, C. and Swan, Y. (2013) Local Pinsker inequalities via Stein’s discrete density approach. IEEE Transactions on Information Theory 59, 5584-5591.
Hallin, M. and Ley, C. (2012) Skew-symmetric distributions and Fisher information - a tale of two densities. Bernoulli 18, 747-763.