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Aboubacar Amiri, Lille U. Print
Thursday, 07 February 2019, 12:15 - 13:15

Aboubacar Amiri, université de Lille

Nonparametric regression for locally stationary functional data

Abstract: We address the problem of nonparametric regression of a real random variable on a non-stationary time series of functional data.
We focus on the estimation of the regression function using a kernel approach. We introduce an estimator of the regression operator which takes into account the non-stationary behavior of the data generating process.
The mean square error and the almost sure convergence of the proposed estimator are derived. Also, a central limit theorem on the regression estimator is established.
Asymptotic results are established with convergence rates, whereas the asymptotic constants are explicitly calculated by assuming that the covariate is local stationary and strong mixing functional process.
The theoretical results are illustrated by means of simulation studies.  

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