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Data-driven rate-optimal specification testing in regression models
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DOI：10.1214/009053604000001200.png)
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摘要
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We propose new data-driven smooth tests for a parametric regression function. The smoothing parameter is selected through a new criterion that favors a large smoothing parameter under the null hypothesis. The resulting test is adaptive rate-optimal and consistent against Pitman local alternatives approaching the parametric model at a rate arbitrarily close to I/root n. Asymptotic critical values come from the standard normal distribution and the bootstrap can be used in small samples. A general formalization allows one to consider a large class of linear smoothing methods, which can be tailored for detection of additive alternatives.
Keyword:
hypothesis testing
nonparametric adaptive tests
selection methods
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3.7
论文数:
2.8K
被引数:
2.9W
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