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Representations, Aproximations and Probabilistics Metrics.
Statistical Applications



The main interest of the group relies on basic research in Probability Theory and Mathematical Statistics. A defining feature of the group is its interest in the analysis of basic concepts in Statistics from different viewpoints (geometric representation and approximation ideas, probabilistic metrics, robustness and stability, etc.). A major goal of the group is the development of new statistical methods with a view towards practical applicability. Currently, principal lines of research focus on the study of robust procedures, model validation, statistical learning (supervised and unsupervised) as well as the related probabilistic foundations. This is a consolidated group with a trajectory that spans over a period of more than 30 years.

Lines of Research

  • Robust statistical methods. Trimming techniques and statistical applications.
  • Study of probability metrics and their statistical applications.
  • Optimal transportation and statistical applications.
  • Resampling-based methods. Bootstrap techniques.
  • Functional data analysis.

Currently, among the procedures we are analyzing, we highlight the methodologies related to:

  • Cluster analysis and mixture models
  • Goodness of fit, essential model validation and analysis of similarity
  • Incomplete transportation problem


Marina Agulló Antolín
Pedro C. Alvárez Esteban
Eustasio del Barrio Tellado
Luis Ángel García Escudero
Alfonso Gordaliza Ramos
Carlos Matrán Bea. (coordinador)
Agustín Mayo Iscar
Juan Antonio Cuesta Albertos (Universidad de Cantabria)