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Nonparametric modelling for functional data: selected survey and tracks for future
N Ling, P Vieu - Statistics, 2018 - Taylor & Francis
Nonparametric functional data analysis is a field whose development started some 15 years
ago and there is a very extensive literature on the topic (hundreds of papers published now) …
ago and there is a very extensive literature on the topic (hundreds of papers published now) …
[HTML][HTML] Data-driven kNN estimation in nonparametric functional data analysis
Kernel nearest-neighbor (k NN) estimators are introduced for the nonparametric analysis of
statistical samples involving functional data. Asymptotic theory is provided for several …
statistical samples involving functional data. Asymptotic theory is provided for several …
Uniform consistency of kNN regressors for functional variables
NL Kudraszow, P Vieu - Statistics & Probability Letters, 2013 - Elsevier
Uniform consistency of kNN regressors for functional variables - ScienceDirect Skip to main
contentSkip to article Elsevier logo Journals & Books Search RegisterSign in View PDF …
contentSkip to article Elsevier logo Journals & Books Search RegisterSign in View PDF …
The k-nearest neighbors method in single index regression model for functional quasi-associated time series data
In the present paper, we consider the k-Nearest Neighbors (k-NN) method in the single
index regression model in the case of a functional predictor and a scalar response. The …
index regression model in the case of a functional predictor and a scalar response. The …
[HTML][HTML] Regression when both response and predictor are functions
F Ferraty, I Van Keilegom, P Vieu - Journal of Multivariate Analysis, 2012 - Elsevier
We consider a nonparametric regression model where the response Y and the covariate X
are both functional (ie valued in some infinite-dimensional space). We define a kernel type …
are both functional (ie valued in some infinite-dimensional space). We define a kernel type …
Classification in general finite dimensional spaces with the k-nearest neighbor rule
S Gadat, T Klein, C Marteau - 2016 - projecteuclid.org
Classification in general finite dimensional spaces with the k-nearest neighbor rule Page 1
The Annals of Statistics 2016, Vol. 44, No. 3, 982–1009 DOI: 10.1214/15-AOS1395 © Institute …
The Annals of Statistics 2016, Vol. 44, No. 3, 982–1009 DOI: 10.1214/15-AOS1395 © Institute …
Additive regression with Hilbertian responses
Supplement to “Additive regression with Hilbertian responses”. The Supplementary Material
[19] contains additional lemmas and propositions with their proofs and the proofs of …
[19] contains additional lemmas and propositions with their proofs and the proofs of …
Asymptotic normality of the k-NN single index regression estimator for functional weak dependence data
In this article, we consider the k-Nearest Neighbors (k-NN) method in a single index
regression model when the explanatory variable is valued in functional space in the setting …
regression model when the explanatory variable is valued in functional space in the setting …
Non-asymptotic uniform rates of consistency for k-nn regression
H Jiang - Proceedings of the AAAI Conference on Artificial …, 2019 - aaai.org
We derive high-probability finite-sample uniform rates of consistency for k-NN regression
that are optimal up to logarithmic factors under mild assumptions. We moreover show that k …
that are optimal up to logarithmic factors under mild assumptions. We moreover show that k …
Locally polynomial Hilbertian additive regression
The supplementary material contains some examples of Hilbert spaces, the simulation
setting, additional simulation results and two additional real data examples. It also includes …
setting, additional simulation results and two additional real data examples. It also includes …