Name
Abstract | ||
|---|---|---|
Kernel nearest-neighbor (kNN) estimators are introduced for the nonparametric analysis of statistical samples involving functional data. Asymptotic theory is provided for several different target operators including regression, conditional density, conditional distribution and hazard operators. The main point of the paper is to consider data-driven methods of selecting the number of neighbors in order to make the proposed methods fully automatic. As a by-product of our proofs we state consistency results for kNN functional estimators which are uniform in the number of neighbors (UINN). Some simulated experiences illustrate the feasibility and the finite-sample behavior of the method. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1016/j.jmva.2016.09.016 | Journal of Multivariate Analysis |
Keywords | Field | DocType |
primary,secondary | Functional data analysis,Kernel (linear algebra),Econometrics,Conditional probability distribution,Regression,Nonparametric statistics,Mathematical proof,Operator (computer programming),Statistics,Mathematics,Estimator | Journal |
Volume | Issue | ISSN |
153 | C | 0047-259X |
Citations | PageRank | References |
6 | 0.63 | 4 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Lydia-Zaitri Kara | 1 | 6 | 0.63 |
| Ali Laksaci | 2 | 11 | 2.08 |
| M. Rachdi | 3 | 33 | 7.25 |
| Philippe Vieu | 4 | 147 | 17.84 |