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2018 Ridge regression for the functional concurrent model
Tito Manrique, Christophe Crambes, Nadine Hilgert
Electron. J. Statist. 12(1): 985-1018 (2018). DOI: 10.1214/18-EJS1412


The aim of this paper is to propose estimators of the unknown functional coefficients in the Functional Concurrent Model (FCM). We extend the Ridge Regression method developed in the classical linear case to the functional data framework. Two distinct penalized estimators are obtained: one with a constant regularization parameter and the other with a functional one. We prove the probability convergence of these estimators with rate. Then we study the practical choice of both regularization parameters. Additionally, we present some simulations that show the accuracy of these estimators despite a very low signal-to-noise ratio.


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Tito Manrique. Christophe Crambes. Nadine Hilgert. "Ridge regression for the functional concurrent model." Electron. J. Statist. 12 (1) 985 - 1018, 2018.


Received: 1 May 2017; Published: 2018
First available in Project Euclid: 15 March 2018

zbMATH: 06864483
MathSciNet: MR3776278
Digital Object Identifier: 10.1214/18-EJS1412

Primary: 62G05 , 62G20 , 62J05
Secondary: 62J07

Keywords: concurrent model , functional data , functional linear model , Ridge regression , varying coefficient model


Vol.12 • No. 1 • 2018
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