Bootstrap independence test for functional linear models

2012/10/03 by González-Manteiga, Wenceslao, González-Rodríguez, Gil, Martínez-Calvo, Adela +1
#62G09 #62G10 #62J05 #FOS: Computer and information sciences #Methodology (stat.ME)

paper · doi:10.48550/arxiv.1210.1072

Abstract

Functional data have been the subject of many research works over the last years. Functional regression is one of the most discussed issues. Specifically, significant advances have been made for functional linear regression models with scalar response. Let (H,) be a separable Hilbert space. We focus on the model Y=+b+ε, where Y and ε are real random variables, X is an H-valued random element, and the model parameters b and Θ are in ℝ and H, respectively. Furthermore, the error satisfies that E(ε|X)=0 and E(ε²|X)=σ²

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