Abstract
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.
Original language | English |
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Pages (from-to) | 725-741 |
Number of pages | 17 |
Journal | Journal of the Royal Statistical Society B: Statistical Methodology |
Volume | 75 |
Issue number | 4 |
Early online date | Mar 2013 |
Publication status | Published - Sep 2013 |
Externally published | Yes |
Austrian Fields of Science 2012
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