The bootstrap in kernel regression for stationary ergodic data when both response and predictor are functions

Johannes T.N. Krebs

Journal of Multivariate Analysis, 2019, vol. 173, issue C, 620-639

Abstract: We consider the double functional regression model Y=r(X)+ε, where the response variable Y is Hilbert space-valued and the covariate X takes values in a pseudometric space. The data satisfy an ergodicity criterion which dates back to Laib and Louani (2010) and are arranged in a triangular array. So our model also applies to samples obtained from spatial processes, e.g., stationary random fields.

Keywords: Confidence sets; Functional spatial processes; Functional time series; Functional kernel regression; Hilbert spaces; Naive bootstrap; Nonparametric regression; Resampling; Stationary ergodic data; Wild bootstrap (search for similar items in EconPapers)
Date: 2019
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Citations: View citations in EconPapers (1)

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DOI: 10.1016/j.jmva.2019.05.004

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