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Title: Flexible Hilbertian additive regression with small errors-in-variables Authors:  Germain Van Bever - Universite de Namur (Belgium) [presenting]
Jeong Min Jeon - Seoul National University (Korea, South)
Abstract: A new framework is presented for additive regression modelling for data in very generic settings. More precisely, we tackle the problem of estimating component functions of additive models where the regressors and/or response variable belong to general Hilbert spaces and can be imperfectly observed. By this, we mean that some variables can be either measured incompletely or with errors. Smooth backfitting methods are used to estimate the component functions consistently and we provide explicit rates of convergence. We amply illustrate our methodology in various settings, including the functional, Riemannian and Hilbertian settings.