Adaptive smoothing spline estimator for the function-on-function linear regression model

Author:

Centofanti FabioORCID,Lepore Antonio,Menafoglio Alessandra,Palumbo Biagio,Vantini Simone

Abstract

AbstractIn this paper, we propose an adaptive smoothing spline (AdaSS) estimator for the function-on-function linear regression model where each value of the response, at any domain point, depends on the full trajectory of the predictor. The AdaSS estimator is obtained by the optimization of an objective function with two spatially adaptive penalties, based on initial estimates of the partial derivatives of the regression coefficient function. This allows the proposed estimator to adapt more easily to the true coefficient function over regions of large curvature and not to be undersmoothed over the remaining part of the domain. A novel evolutionary algorithm is developed ad hoc to obtain the optimization tuning parameters. Extensive Monte Carlo simulations have been carried out to compare the AdaSS estimator with competitors that have already appeared in the literature before. The results show that our proposal mostly outperforms the competitor in terms of estimation and prediction accuracy. Lastly, those advantages are illustrated also in two real-data benchmark examples. The AdaSS estimator is implemented in the package , openly available online on CRAN.

Publisher

Springer Science and Business Media LLC

Subject

Computational Mathematics,Statistics, Probability and Uncertainty,Statistics and Probability

Reference51 articles.

1. Abramovich F, Steinberg DM (1996) Improved inference in nonparametric regression using lk-smoothing splines. J Stat Plann Inference 49(3):327–341

2. Bäck T, Fogel DB, Michalewicz Z (1997) Handbook of evolutionary computation. Chapman and Hall/CRC, UK

3. Bergstra J, Bengio Y (2012) Random search for hyper-parameter optimization. J Mach Learn Res 13(10):281–305

4. Bergstra J, Bardenet R, Bengio Y, Kégl B (2011) Algorithms for hyper-parameter optimization. Adv Neural Inf Process Syst, Curran Assoc Inc 24:871

5. Canale A, Vantini S (2016) Constrained functional time series: applications to the italian gas market. Int J Forecast 32(4):1340–1351

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