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- W2049993661 abstract "This paper focuses on deriving explicit formulae of the asymptotic variance in nonparametric regression with fractional time-series errors. Unified formulae are obtained for fixed design models with long-memory, short-memory and antipersistent errors. It is also found that in strongly antipersistent case the Uniform kernel is no longer the minimum variance one and that for a fourth-order kernel the constant in the asymptotic variance for long-memory errors may be clearly smaller than that for i.i.d. errors. The results are applied to improving an existing data-driven algorithm. Practical performance of the proposed algorithm is illustrated with simulated and real data examples." @default.
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- W2049993661 date "2007-02-01" @default.
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- W2049993661 title "On the asymptotic variance in nonparametric regression with fractional time-series errors" @default.
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- W2049993661 doi "https://doi.org/10.1080/10485250701381737" @default.
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