Semi-functional partial linear regression

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Nonparametric time series prediction: A semi-functional partial linear modeling (2008, result): “Remark 4 From Theorems 1 and 2 together with the results by Aneiros-Pérez and Vieu [2], we can say that the same rates of convergence are observed as in the setting of mixing dependence as in the setting of independence (but the e¤ect of the dependence is present through the conditions imposed on th”
Statistical Computing in Functional Data Analysis: The R Package fda.usc (2012, methodology): “Following the ideas by Aneiros-Pérez and Vieu (2006), we calculated the mean square error of prediction (MEP): MEP = (∑n i=1 (yi − ŷi) 2 /n ) / (Var (y)), which is used for comparing the predictions of different fitted models.”
Nonparametric modelling for functional data: selected survey and tracks for future (2018, methodology): “the additive model [63] and sparse regression models [141,142,151] .”
Empirical likelihood confidence intervals for nonparametric functional data analysis (2009, methodology): “Besides, we use the protein and moisture content as two covariates of the linear part in a partially linear model since a previous study (Aneiros-Perez and Vieu, 2006) shows these additional covariates give better prediction performance.”