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Error-dependent smoothing rules in local linear regression

Cheng, M-Y; Hall, Peter


We suggest an adaptive, error-dependent smoothing method for reducing the variance of local-linear curve estimators. It involves weighting the bandwidth used at the ith datum in proportion to a power of the absolute value of the ith residual. We show that the optimal power is 2/3. Arguing in this way, we prove that asymptotic variance can be reduced by 24% in the case of Normal errors, and by 35% for double-exponential errors. These results might appear to violate Jianqing Fan's bounds on...[Show more]

CollectionsANU Research Publications
Date published: 2002
Type: Journal article
Source: Statistica Sinica


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