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Bandwidth selection for local linear regression smoothers
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DOI：10.1111/1467-9868.00361.png)
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摘要
En 中文
The paper presents a general strategy for selecting the bandwidth of nonparametric regression estimators and specializes it to local linear regression smoothers. The procedure requires the sample to be divided into a training sample and a testing sample. Using the training sample we first compute a family of regression smoothers indexed by their bandwidths. Next we select the bandwidth by minimizing the empirical quadratic prediction error on the testing sample. The resulting bandwidth satisfies a finite sample oracle inequality which holds for all bounded regression functions. This permits asymptotically optimal estimation for nearly any regression function. The practical performance of the method is illustrated by a simulation study which shows good finite sample behaviour of our method compared with other bandwidth selection procedures.
Keyword:
local linear regression smoother
nonparametric regression
oracle inequality
universal bandwidth selection
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IF:
3.6
论文数:
1.5K
被引数:
3.2W
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