Abstract
Abstract An important aspect of nonparametric regression by spline smoothing is the estimation of the smoothing parameter. In this article we report on an extensive simulation study that investigates the finite-sample performance of generalized cross-validation, cross-validation, and marginal likelihood estimators of the smoothing parameter in splines of orders 2 and 3. The performance criterion for both the estimate of the function and its first derivative is measured by the square root of integrated squared error. Marginal likelihood using splines of degree 5 emerges as an attractive alternative to the other estimators in that it usually outperforms them and is also faster to compute.
Keywords
EstimatorSmoothingSmoothing splineMathematicsCross-validationSpline (mechanical)StatisticsMean squared errorNonparametric statisticsMarginal likelihoodKernel smootherNonparametric regressionRestricted maximum likelihoodApplied mathematicsMaximum likelihoodComputer scienceSpline interpolationKernel methodEngineeringArtificial intelligence
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Publication Info
- Year
- 1991
- Type
- article
- Volume
- 86
- Issue
- 416
- Pages
- 1042-1050
- Citations
- 79
- Access
- Closed
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Cite This
Robert Kohn,
Craig F. Ansley,
David Tharm
(1991).
The Performance of Cross-Validation and Maximum Likelihood Estimators of Spline Smoothing Parameters.
Journal of the American Statistical Association
, 86
(416)
, 1042-1050.
https://doi.org/10.1080/01621459.1991.10475150
Identifiers
- DOI
- 10.1080/01621459.1991.10475150