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• W4283803670 abstract "A new estimator, named S-LASSO, is proposed for the coefficient function of the Function-on-Function linear regression model. The S-LASSO estimator is shown to be able to increase the interpretability of the model, by better locating regions where the coefficient function is zero, and to smoothly estimate non-zero values of the coefficient function. The sparsity of the estimator is ensured by a textit{functional LASSO penalty}, which pointwise shrinks toward zero the coefficient function, while the smoothness is provided by two roughness penalties that penalize the curvature of the final estimator. The resulting estimator is proved to be estimation and pointwise sign consistent. Via an extensive Monte Carlo simulation study, the estimation and predictive performance of the S-LASSO estimator are shown to be better than (or at worst comparable with) competing estimators already presented in the literature before. Practical advantages of the S-LASSO estimator are illustrated through the analysis of the textit{Canadian weather}, textit{Swedish mortality} and textit{ship COtextsubscript{2} emission data}. The S-LASSO method is implemented in the textsf{R} package textsf{slasso}, openly available online on CRAN." @default.
• W4283803670 created "2022-07-05" @default.
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• W4283803670 date "2022-12-01" @default.
• W4283803670 modified "2023-10-16" @default.
• W4283803670 title "Smooth LASSO estimator for the Function-on-Function linear regression model" @default.
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• W4283803670 doi "https://doi.org/10.1016/j.csda.2022.107556" @default.
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