Kernel estimation for additive models under dependence
Nonparametric estimation of the conditional mean function for additive models is investigated in cases where the observed data are dependent. We use an additive kernel estimator which is a sum of Nadaraya--Watson estimators. Under a strong mixing condition, the kernel estimator is shown to be asymptotically normal and to achieve the univariate optimal rate of convergence in mean squared error.
Year of publication: |
1993
|
---|---|
Authors: | Baek, Jangsun ; Wehrly, Thomas E. |
Published in: |
Stochastic Processes and their Applications. - Elsevier, ISSN 0304-4149. - Vol. 47.1993, 1, p. 95-112
|
Publisher: |
Elsevier |
Keywords: | mixing conditions nonparametric regression optimal rate of convergence time series Nadaraya-Watson estimator |
Saved in:
Online Resource
Saved in favorites
Similar items by person
-
Clustering of high-dimensional data via finite mixture models
McLachlan, Geoff J., (2010)
-
Testing the equality of two regression curves using linear smoothers
King, Eileen, (1991)
-
Bias Robust Estimation in Finite Populations Using Nonparametric Calibration
Chambers, Raymond L., (1993)
- More ...