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Kernel estimation for additive models under dependence

Jangsun Baek and Thomas E. Wehrly

Stochastic Processes and their Applications, 1993, vol. 47, issue 1, 95-112

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.

Keywords: mixing; conditions; nonparametric; regression; optimal; rate; of; convergence; time; series; Nadaraya--Watson; estimator (search for similar items in EconPapers)
Date: 1993
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Citations: View citations in EconPapers (3)

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