Jean-Michel Loubes
Professor of Mathematics, Université Toulouse Paul Sabatier, Institut de Mathématiques de Toulouse. Equipe de Statistique et Optimisation & Projet AOC CIMI (Apprentissage, Optimisation et Complexité) Chair Fair and Robust Machine Learning at Artificial and Natural Intelligence Toulouse Institute (ANITI)
Recent Publications • E. Del Barrio and J.-M. Loubes. Central limit theorems for empirical transportation cost in general dimension. Annals of Probability arXiv preprint arXiv :1705.01299, to appear 2018. • F. Bachoc, F. Gamboa, J-M. Loubes, N. Venet. Gaussian Process Regression Model for Distribution Inputs IEEE Trans. Inform. Theory, to appear, 2018. • E. Del Barrio, P. Gordaliza, H. Lescornel, and J.-M. Loubes. A statistical analysis of a deformation model with wasserstein barycenters : estimation procedure and goodness of t test. Journal of Multivariate Analysis, to appear 2018. • C. Barreyre, B. Laurent, J.-M. Loubes, B. Cabon, and L. Boussouf. Multiple testing for outlier detection in functional data. Space Ops bests student paper award, 2018.T. Le Gouic and J.-M. Loubes. Existence and consistency of wasserstein barycenters. Probability Theory and Related Fields, 168(3-4) :901{917, 2017. • D. Velandia, F. Bachoc, M. Bevilacqua, X. Gendre, J.-M. Loubes, Maximum likelihood estimation for a bivariate gaussian process under xed domain asymptotics. Electronic Journal of Statistics, 11(2) :2978{3007, 2017. • J.-M. Loubes, B. Pelletier, Prediction by quantization of a conditional distribution. Electronic journal of statistics, 11(1) :2679{2706, 2017. P. C. Besse, B. Guillouet, J.-M. Loubes, and F. Royer. Destination prediction by trajectory distribution based model. IEEE Transactions on Intelligent Transportation Systems, 2017.
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Articles by Jean-Michel Loubes
Risk measures: a generalization from the univariate to the matrix-variate
This paper develops a method for estimating value-at-risk and conditional value-at-risk when the underlying risk factors follow a beta distribution in a univariate and a matrix-variate setting.