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Distribution regression model with a Reproducing Kernel Hilbert Space approach

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  • Bui Thi Thien Trang
  • Jean-Michel Loubes
  • Laurent Risser
  • Patricia Balaresque

Abstract

In this paper, we introduce a new distribution regression model for probability distributions. This model is based on a Reproducing Kernel Hilbert Space (RKHS) regression framework, where universal kernels are built using Wasserstein distances for distributions belonging to W2(Ω) and Ω is a compact subspace of R. We prove the universal kernel property of such kernels and use this setting to perform regressions on functions. Different regression models are first compared with the proposed one on simulated functional data for both one-dimensional and two-dimensional distributions. We then apply our regression model to transient evoked otoascoutic emission (TEOAE) distribution responses and real predictors of the age.

Suggested Citation

  • Bui Thi Thien Trang & Jean-Michel Loubes & Laurent Risser & Patricia Balaresque, 2021. "Distribution regression model with a Reproducing Kernel Hilbert Space approach," Communications in Statistics - Theory and Methods, Taylor & Francis Journals, vol. 50(9), pages 1955-1977, May.
    • Handle: RePEc:taf:lstaxx:v:50:y:2021:i:9:p:1955-1977
    DOI: 10.1080/03610926.2019.1658782
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