Name
Abstract | ||
|---|---|---|
Kernel estimates of a regression operator are investigated when the explanatory variable is of functional type. The bandwidths are locally chosen by a data-driven method based on the minimization of a functional version of a cross-validated criterion. A short asymptotic theoretical support is provided and the main body of this paper is devoted to various finite sample size applications. In particular, it is shown through some simulations, that a local bandwidth choice enables to capture some underlying heterogeneous structures in the functional dataset. As a consequence, the estimation of the relationship between a functional variable and a scalar response, and hence the prediction, can be significantly improved by using local smoothing parameter selection rather than global one. This is also confirmed from a chemometrical real functional dataset. These improvements are much more important than in standard finite dimensional setting. |
Year | DOI | Venue |
|---|---|---|
2007 | 10.1007/s00180-007-0045-0 | Computational Statistics |
Keywords | DocType | Volume |
local smoothing parameter selection,functional version,functional variable,various finite sample size,local bandwidth choice,local smoothing regression,cross-validation · functional data · local versus global bandwidths · regression operator,standard finite dimensional setting,functional dataset,explanatory variable,chemometrical real functional dataset,functional type | Journal | 22 |
Issue | ISSN | Citations |
3 | 1613-9658 | 8 |
PageRank | References | Authors |
1.06 | 0 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| K. Benhenni | 1 | 12 | 3.07 |
| F. Ferraty | 2 | 91 | 21.33 |
| M. Rachdi | 3 | 33 | 7.25 |
| P. Vieu | 4 | 87 | 20.78 |