Ling, NengxiangAneiros, GermánVieu, Philippe2026-09-172026-09-172020Ling, N., Aneiros, G. & Vieu, P. kNN estimation in functional partial linear modeling. Stat Papers 61, 423–444 (2020). https://doi.org/10.1007/s00362-017-0946-01613-9798https://hdl.handle.net/2183/49291This version of the article has been accepted for publication, after peer review (when applicable) and is subject to Springer Nature’s AM terms of use, but is not the Version of Record and does not reflect post-acceptance improvements, or any corrections. The Version of Record is available online at: https://doi.org/10.1007/s00362-017-0946-0[Abstract]: A statistical procedure combining the local adaptivity and the easiness of implementation of k-nearest-neighbours (kNN) estimates together with the semiparametric flexibility of partial linear modeling is developed for regression problems involving functional variable. Various asymptotic results are stated, both for the linear parameters and for the nonparametric operator involved in the model. A simulation study compares the finite sample behaviour of the kNN method with alternative estimation procedures. Finally, comparison with alternative functional regression models is carried out by means of a real curves data application which exhibits the interest both of the kNN method and of the semi-parametric modeling.engCopyright © 2017, Springer-Verlag GmbH GermanykNN estimateFunctional data analysisPartial linear regressionSemi-parametricsjournal articleopen access10.1007/s00362-017-0946-0