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Kernel estimation for additive models under dependence

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  • Baek, Jangsun
  • Wehrly, Thomas E.

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.

Suggested Citation

  • Baek, Jangsun & Wehrly, Thomas E., 1993. "Kernel estimation for additive models under dependence," Stochastic Processes and their Applications, Elsevier, vol. 47(1), pages 95-112, August.
    • Handle: RePEc:eee:spapps:v:47:y:1993:i:1:p:95-112
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    Cited by:

    1. Graciela Boente & Alejandra Martínez, 2017. "Marginal integration M-estimators for additive models," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 26(2), pages 231-260, June.
    2. Marie Hušková & Matúš Maciak, 2017. "Discontinuities in robust nonparametric regression with α-mixing dependence," Journal of Nonparametric Statistics, Taylor & Francis Journals, vol. 29(2), pages 447-475, April.
    3. Graciela Boente & Alejandra Martínez & Matías Salibián-Barrera, 2017. "Robust estimators for additive models using backfitting," Journal of Nonparametric Statistics, Taylor & Francis Journals, vol. 29(4), pages 744-767, October.

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