References
- Craven, P., and Wahba, G. (1979), “Smoothing Noisy Data with Spline Functions,” Numerical Mathematics, 31, 377–403.
- Friedman, J. H. (1991), “Multivariate Adaptive Regression Splines” (with discussion), Annals of Statistics, 19, 1–67.
- Gu, C. (2009), “General Smoothing Splines,” available at http://cran.r-project.org.
- ——— (2013), Smoothing Spline ANOVA Models (Vol. 297), Springer Science & Business Media.
- Guha, S., Hafen, R., Rounds, J., Xia, J., Li, J., Xi, B., and Cleveland, W. S. (2012), “Large Complex Data: Divide and Recombine (D&R) with Rhipe,” Stat, 1, 53–67.
- Guha, S., Kidwell, P., Hafen, R. P., and Cleveland, W. S. (2009), “Visualization Databases for the Analysis of Large Complex Datasets,” Journal of Machine Learning Research, 5, 193–200.
- Kim, Y.-J., and Gu, C. (2004), “Smoothing Spline Gaussian Regression: More Scalable Computation via Efficient Approximation,” Journal of the Royal Statistical Society, Series B, 66, 337–356.
- Kimeldorf, G. S., and Wahba, G. (1970), “A Correspondence Between Bayesian Estimation on Stochastic Processes and Smoothing by Splines,” The Annals of Mathematical Statistics, 41, 495–502.
- Kou, S., and Efron, B. (2002), “Smoothers and the Cp, GML and EE Criteria: A Geometric Approach,” Journal of the American Statistical Association, 97, 766–782.
- Liu, Z., and Guo, W. (2010), “Data Driven Adaptive Spline Smoothing,” Statistica Sinica, 20, 1143–1163.
- Luo, Z., and Wahba, G. (1997), “Hybrid Adaptive Splines,” Journal of the American Statistical Association, 92, 107–116.
- Ma, P., Huang, J. Z., and Zhang, N. (2015), “Efficient Computation of Smoothing Splines via Adaptive Basis Sampling,” Biometrika, 102, 631–645.
- Nychka, D. (1993), “Splines as Local Smoothers,” Annals of Statistics, 88, 415–428.
- Shang, Z., and Cheng, G. (2017), “Computational Limits of a Distributed Algorithm for Smoothing Spline,” The Journal of Machine Learning Research, 18(1), 3809–3845.
- Sklar, J., Wu, J., Meiring, W., and Wang, Y. (2013), “Non-Parametric Regression with Basis Selection from Multiple Libraries,” Technometrics, 55, 189–201.
- Wahba, G. (1985), “A Comparison of GCV and GML for Choosing the Smoothing Parameters in the Generalized Spline Smoothing Problem,” Annals of Statistics, 4, 1378–1402.
- ——— (1990), Spline Models for Observational Data (Vol. 59), Philadelphia, PA: SIAM.
- Wang, Y. (2011), Smoothing Splines: Methods and Applications, Boca Raton, FL: CRC Press.
- Wang, Y., and Ke, C. (2002), “ASSIST: A Suite of S-Plus Functions Implementing Spline Smoothing Techniques,” available at http://cran.r-project.org.
- Wood, S. (2011), “mgcv: Mixed GAM Computation Vehicle with GCV/AIC/REML Smoothness Estimation,” available at http://cran.r-project.org.
- Zhang, Y., Duchi, J. C., and Wainwright, M. J. (2013), “Divide and Conquer Kernel Ridge Regression,” The Journal of Machine Learning Research Proceedings, 30, 592–617.