66th ISI World Statistics Congress

66th ISI World Statistics Congress

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On regression and prediction methods involving non-euclidean observations.

Organiser

AY
Anne-Françoise Yao

Participants

  • VM
    Vincent Monsan     (Chair)

  • FN
    Dr Florence Nicol     (Presenter/Speaker)
  • Some asymptotic properties of the kernel Frechet Intrinsic regression on Riemannian manifolds.

  • DK
    Djack Guy Aude Kouadio     (Presenter/Speaker)
  • Kernel density and regression for continuous time process.

  • WN
    Wiem Nefzi     (Presenter/Speaker)
  • On Kernel regression estimation estimation for dependent data in riemannian manifold.

  • PM
    Papa Alioune Meïssa Mbaye     (Presenter/Speaker)
  • (with Wiem Nefzi)

  • GO
    Gaetan Otogo     (Presenter/Speaker)
  • On regression and prediction on a graph based on by Physics-Informed models with application to Electric power grids issue. (with Koudou Efeovi)

  • TK
    Tanon Lambert Kadjo     (Discussant)

  • MB
    Marcellin Brou     (Discussant)

  • AY
    Prof. Anne-Françoise Yao     (Discussant)

    • Conference

    66th ISI World Statistics Congress

    Category: International Statistical Institute

    Proposal Description

    The problems of regression and prediction have been widely addressed in situations where the dataset lies in Euclidean space. However, in many fields of data science, observations lie in a non-Euclidean space. There is very limited literature in statistical mathematics dedicated to the non-Euclidean case. This session address some issues depending on if the output and/or the input belongs to a non-Euclidean space.

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