Recursive local polynomial regression under dependence conditions
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Recursive local polynomial regression under dependence conditionsDate
2000Citation
Test, vol. 9, n. 1, pp. 209-232
Abstract
In the case of the random design nonparametric regression, one recursive local polynomial smoother is considered. Expressions for the bias and the variance matrix of the estimators of the regression function and its derivatives are obtained under dependence conditions (strongly mixing processes). The obtained Mean Squared Error is shown to be larger than those of the analogous nonrecursive regression estimators, although retaining the same convergence rate. The properties of strong consistency with convergence rates are established for the proposed estimators. Finally, in order to analyse the influence of both the sample size and the dependence in the behaviour of the proposed recursive estimator, a simulation study is performed.
Keywords
Local polynomial fitting
Recursive nonparametric estimation
Strongly mixing process
Recursive nonparametric estimation
Strongly mixing process
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The original publication is available at www.springerlink.com
ISSN
1133-0686