Hypothesis testing in smoothing spline models

Abstract

Nonparametric regression models are often used to check or suggest a parametric model. Several methods have been proposed to test the hypothesis of a parametric regression function against an alternative smoothing spline model. Some tests such as the locally most powerful (LMP) test by Cox et al. (Cox, D., Koh, E., Wahba, G. and Yandell, B. (1988). Testing the (parametric) null model hypothesis in (semiparametric) partial and generalized spline models. Ann. Stat., 16, 113–119.), the generalized maximum likelihood (GML) ratio test and the generalized cross validation (GCV) test by Wahba (Wahba, G. (1990). Spline models for observational data. CBMS-NSF Regional Conference Series in Applied Mathematics, SIAM.) were developed from the corresponding Bayesian models. Their frequentist properties have not been studied. We conduct simulations to evaluate and compare finite sample performances. Simulation results show that the performances of these tests depend on the shape of the true function. The LMP and GML tests are more powerful for low frequency functions while the GCV test is more powerful for high frequency functions. For all test statistics, distributions under the null hypothesis are complicated. Computationally intensive Monte Carlo methods can be used to calculate null distributions. We also propose approximations to these null distributions and evaluate their performances by simulations.

E-mail: [email protected]

E-mail: [email protected]

Keywords:

• Bayesian models for smoothing splines
• Connections between linear mixed effects models and smoothing splines
• GCV test
• GML test
• F-test
• LMP test
• Symmetrized Kullback-Leibler test

Acknowledgements

This work was supported by NIH grants R01 GM58533. The authors thank the associate editor and a referee for their useful comments that have greatly improved this article.

Notes

E-mail: [email protected]

E-mail: [email protected]

E-mail: [email protected]