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Statistics for Hilbert and functional spaces

Data in many different experiments cannot be described by a single numerical value but through a curve. In most of situations, from a statistical view point, these curves can be considered as random elements in an appropriate functional space that usually is a separable Hilbert space. This is the case of functional data obtained by continuous/sparse-time/spatial monitoring processes, or of fuzzy data obtained through sociological surveys, to name but a few. Applications are found in industrial processes as well as in different sciences, such as econometrics, medicine, sociology, biostatistics, bioinformatics, environmetrics, geophysics, chemometrics, etc.

This track is concerned with the statistical analysis of functional data and more generally of random elements in Functional and separable Hilbert spaces.

Gil Gonzalez-Rodriguez, University of Oviedo, Spain
Organized Sessions associated with this Track
  • E023: Foundations for depth methods in multivariate and functional data settings
    Organizers: Robert Serfling
  • E031: Complex and next generation functional data analysis
    Organizers: Jane-Ling Wang
  • E051: Advances and applications of functional data analysis
    Organizers: Ming-Yen Cheng
  • E109: Some new development in functional data analysis
    Organizers: Catherine Chunling Liu
  • E117: Functional data analysis
    Organizers: Alicia Nieto-Reyes
  • E225: Inference for functional data, with life sciences applications
    Organizers: Laura Sangalli
  • E265: Nonparametric functional data analysis
    Organizers: Germain Van Bever
  • E541: High-dimensional or multivariate functional data analysis
    Organizers: Juhyun Park
  • E620: Non- and semi-parametric approaches in functional statistics
    Organizers: Enea Bongiorno