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Smoothing parameter selection in two frameworks for penalized splines
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DOI：10.1111/rssb.12010.png)
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摘要
En 中文
There are two popular smoothing parameter selection methods for spline smoothing. First, smoothing parameters can be estimated by minimizing criteria that approximate the average mean-squared error of the regression function estimator. Second, the maximum likelihood paradigm can be employed, under the assumption that the regression function is a realization of some stochastic process. The asymptotic properties of both smoothing parameter estimators for penalized splines are studied and compared. A simulation study and a real data example illustrate the theoretical findings.
Keyword:
Average mean-squared error minimizer
Maximum likelihood
Oracle parameters
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期刊
J
IF:
3.6
论文数:
1.5K
被引数:
3.2W
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