ABSTRACT
This paper develops a new automatic and location-adaptive procedure for estimating regression in a Functional Single-Index Model (FSIM). This procedure is based on k-Nearest Neighbours (kNN) ideas. The asymptotic study includes results for automatically data-driven selected number of neighbours, making the procedure directly usable in practice. The local feature of the kNN approach insures higher predictive power compared with usual kernel estimates, as illustrated in some finite sample analysis. As by-product, we state as preliminary tools some new uniform asymptotic results for kernel estimates in the FSIM model.
Acknowledgments
The authors wish to thank two anonymous referees for their helpful comments and suggestions, which greatly improved the quality of this paper.
Disclosure statement
No potential conflict of interest was reported by the authors.