J
Jean-Michel Loubes
Researcher at Institut de Mathématiques de Toulouse
Publications - 203
Citations - 10539
Jean-Michel Loubes is an academic researcher from Institut de Mathématiques de Toulouse. The author has contributed to research in topics: Estimator & Inverse problem. The author has an hindex of 23, co-authored 184 publications receiving 9133 citations. Previous affiliations of Jean-Michel Loubes include Centre national de la recherche scientifique & Département de Mathématiques.
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Journal ArticleDOI
Least angle regression
Bradley Efron,Trevor Hastie,Iain M. Johnstone,Robert Tibshirani,Hemant Ishwaran,Keith Knight,Jean-Michel Loubes,Jean-Michel Loubes,Pascal Massart,Pascal Massart,David Madigan,David Madigan,Greg Ridgeway,Greg Ridgeway,Saharon Rosset,Saharon Rosset,Ji Zhu,Robert A. Stine,Berwin A. Turlach,Sanford Weisberg +19 more
TL;DR: A publicly available algorithm that requires only the same order of magnitude of computational effort as ordinary least squares applied to the full set of covariates is described.
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Discussion of "least angle regression" by efron et al.
TL;DR: The LARS method as discussed by the authors is based on a recursive procedure selecting, at each step, the covariates having largest absolute correlation with the response variable, which enables recovering the estimates given by the Lasso and Stagewise.
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Review and Perspective for Distance-Based Clustering of Vehicle Trajectories
TL;DR: A new distance is introduced: symmetrized segment-path distance (SSPD), which is compared to their corresponding clustering results obtained using both the hierarchical clustering and affinity propagation methods.
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Existence and Consistency of Wasserstein Barycenters
TL;DR: In this article, the existence of Wasserstein barycenters of random distributions defined on a geodesic space was proved and the consistency of this barycenter in a general setting was established.
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Distribution's template estimate with Wasserstein metrics
TL;DR: In this article, the authors tackle the problem of comparing distributions of random variables and defining a mean pattern between a sample of random events using barycenters of measures in the Wasserstein space, and propose an iterative version as an estimation of the mean distribution.