KERNEL ESTIMATION FOR ADDITIVE-MODELS UNDER DEPENDENCE Academic Article

abstract

• 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. 1993.

authors

• Wehrly, Thomas

published proceedings

• STOCHASTIC PROCESSES AND THEIR APPLICATIONS

author list (cited authors)

• BAEK, J., & WEHRLY, T. E.

citation count

• 10

complete list of authors

• BAEK, J||WEHRLY, TE

publication date

• August 1993

publisher

• Elsevier  Publisher

published in

• Stochastic Processes and their Applications  Journal

keywords

• Mixing Conditions
• Nadaraya-watson Estimator
• Nonparametric Regression
• Optimal Rate Of Convergence
• Time Series

Digital Object Identifier (DOI)

• 10.1016/0304-4149(93)90096-M

start page

• 95

end page

• 112

volume

• 47

issue

• 1

URL

• http://dx.doi.org/10.1016/0304-4149(93)90096-m