Gaussian Process Regression Analysis for Functional Data

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Regression imputation in the functional linear model with missing values in the response (2019, methodology): “Nonparametric statistical methods for functional regression analysis, specifically the methods based on a Gaussian process prior in a functional space are discussed in [27].”
Operator-valued Kernels for Learning from Functional Response Data (2015, background): “To this end, we extend the traditional scalar-valued attribute setting to a function-valued attribute setting.”
Nonparametric modelling for functional data: selected survey and tracks for future (2018, background): “The books by Bosq [2], Ramsay and Silverman [3], Ferraty and Vieu [4], Bosq and Blanke [5], Shi and Choi [6], Horváth and Kokoszka [7], Zhang [8], Bongiorno et al.”
A general framework for functional regression modelling (2017, methodology): “A fourth approach is based on GP regression models (e.g., Shi et al., 2007; Shi and Choi, 2011) and directly models the observed functional data as realizations from such a GP with a covariance kernel from a known parametric family that typically incorporates covariate effects and linear effects of ”