Random-Design Regression under Long-Range Dependent Errors

We consider the random-design nonparametric regression model with long-range dependent errors that may also depend on the independent and identically distributed explanatory variables. Disclosing a smoothing dichotomy, we show that the finite-dimensional distributions of the Nadaraya-Watson kernel e...

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Bibliographic Details
Published in:Bernoulli : official journal of the Bernoulli Society for Mathematical Statistics and Probability 1999-04, Vol.5 (2), p.209-224
Main Authors: Csörgö, Sándor, Mielniczuk, Jan
Format: Article
Language:English
Subjects:
asymptotic finite-dimensional distributions
Data smoothing
Density estimation
Determinism
Estimators
kernel estimators
Linear regression
long-range and short-range dependent errors
Perceptron convergence procedure
Random variables
random-design regression
Regression analysis
smoothing dichotomy
Time dependence
Time series
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Summary:We consider the random-design nonparametric regression model with long-range dependent errors that may also depend on the independent and identically distributed explanatory variables. Disclosing a smoothing dichotomy, we show that the finite-dimensional distributions of the Nadaraya-Watson kernel estimator of the regression function converge either to those of a degenerate process with completely dependent marginals or to those of a Gaussian white-noise process. The first case occurs when the bandwidths are large enough in a specified sense to allow long-range dependence to prevail. The second case is for bandwidths that are small in the given sense, when both the required norming sequence and the limiting process are the same as if the errors were independent. This conclusion is also derived for all bandwidths if the errors are short-range dependent. The borderline situation results in a limiting convolution of the two cases. The main results contrast with previous findings for deterministic-design regression.
ISSN:1350-7265
DOI:10.2307/3318432