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Title: Additive regression with metric-spaced-valued predictors and Hilbertian responses Authors:  Jeong Min Jeon - KU Leuven (Belgium) [presenting]
Byeong Park - Seoul National University (Korea, South)
Ingrid Van Keilegom - KU Leuven (Belgium)
Abstract: Nonparametric additive regression with general metric-space-valued predictors and general Hilbertian responses is considered. We estimate the component maps of the additive models by the smooth backfitting method. We present a general asymptotic theory and apply the theory to finite-dimensional Hilbertian predictors and manifold-valued predictors. Those predictors include not only standard Euclidean predictors but also compositional, circular, spherical and shape predictors. Our numerical study shows its wide applications.