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Article Dans Une Revue Canadian Journal of Statistics Année : 2018

Distribution regression model with a Reproducing Kernel Hilbert Space approach

Patricia Balaresque

Résumé

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 W 2 (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. We then apply our regression model to transient evoked otoascoutic emission (TEOAE) distribution responses and real predictors of the age.
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Dates et versions

hal-01824022 , version 1 (26-06-2018)

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Thi Thien Trang Bui, Jean-Michel Loubes, Laurent Risser, Patricia Balaresque. Distribution regression model with a Reproducing Kernel Hilbert Space approach. Canadian Journal of Statistics, 2018. ⟨hal-01824022⟩
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