Convergence rate of kernel regression estimation for time series data when both response and covariate are functional

Nengxiang Ling (), Lingyu Wang () and Philippe Vieu ()
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Nengxiang Ling: Hefei University of Technology
Lingyu Wang: Hefei University of Technology
Philippe Vieu: Université Paul Sabatier

Metrika: International Journal for Theoretical and Applied Statistics, 2020, vol. 83, issue 6, No 5, 713-732

Abstract: Abstract We investigate kernel estimates in the functional nonparametric regression model when both the response and the explanatory variable (the covariate) are functional. The rates of almost complete and uniform almost complete convergence of the estimator are obtained under some mild $$\alpha $$α-mixing functional sample. Finally, a simulation study is carried out to illustrate the finite sample performance of the estimator.

Keywords: Functional data analysis; Functional kernel regression estimator; Strong mixing dependence; Convergence rate (search for similar items in EconPapers)
Date: 2020
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DOI: 10.1007/s00184-019-00757-y

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