Kernel Estimators for Additive Models with Dependent Observations from Random Fields |
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Authors: | Jiexiang Li |
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Affiliation: | 1. Department of Mathematics , College of Charleston , Charleston , South Carolina , USA lij@cofc.edu |
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Abstract: | Nonparametric estimation of the regression function for additive models is investigated in cases where the observed data are dependent. An additive kernel estimator for the regression function under some general mixing conditions is proposed. Under the mixing conditions, the additive kernel estimator is shown to be asymptotically normal. |
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Keywords: | Additive model Asymptotic normality Nadaraya-watson estimator Random field |
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