Selecting the number of knots for penalized splines

Journal of Computational and Graphical Statistics

Volumn 11, Issue 4, 2002, Pages 735-757

  Ruppert, David ^a  

^a Cornell University   (United States)

Author keywords

Additive models; Full search; Myopic search; P spline; Smoothing

Indexed keywords

EID: 0036921633     PISSN: 10618600     EISSN: 15372715     Source Type: Journal    
DOI: 10.1198/106186002853     Document Type: Article

References (20)

1. Chambers, J. M., and Hastie, T. J. (1993), Statistical Models in S, New York and London: Chapman & Hall.
2. Chaudhuri, P., and Marron, J. S. (1999), “SiZer for Exploration of Structures in Curves,” Journal of the American Statistical Association, 94, 807-823.
3. Denison, D. G. T., Mallick, B. K., and Smith, A. F. M. (1998), “Automatic Bayesian Curve Fitting,” Journal of the Royal Statistical Society, Series B, 60, 333-350.
4. Eilers, P. H. C., and Marx, B. D. (1996), “Flexible Smoothing With B-splines and Penalties” (with discussion), Statistical Science, 11, 89-121.
5. Eubank, R. L. (1988), Spline Smoothing and Nonparametric Regression, New York and Basil: Marcel Dekker.
6. Friedman, J. H. (1991), “Multivariate Adaptive Regression Splines” (with discussion), The Annals of Statistics, 19, 1-141.
7. Friedman, J. H., and Silverman, B. W. (1989), “Flexible Parsimonious Smoothing and Additive Modeling” (with discussion), Technometrics, 31, 3-39.
8. Gill, P. E., Murray, W., and Wright, M. H. (1981), Practical Optimization, New York, Academic Press.
9. Hastie, T. (1996), “Pseudosplines,” Journal of the Royal Statistical Society, Series B, 58, 379-396.
10. Hastie, T., and Tibshirani, R. (1990), Generalized Additive Models, London: Chapman and Hall.
11. Luo, Z., and Wahba, G. (1997), “Hybrid Adaptive Splines,” Journal of the American Statistical Association, 92, 107-116.
12. Marx B. D., and Eilers P. H. C. (1998), “Direct Generalized Additive Modeling with Penalized Likelihood,” Computational Statistics and Data Analysis, 28, 193-209.
13. Nychka, D. (2000), “Spatial Process Estimates as Smoothers,” Smoothing and Regression. Approaches, Computation and Application, ed. M. G. Schimek, New York: Wiley, pp. 393-424.
14. O’Sullivan, F. (1986), “A Statistical Perspective on Ill-Posed Inverse Problems” (with discussion), Statistical Science, 1, 505-527.
15. O’Sullivan, F. (1988), “Fast Computation of Fully Automated Log-Density and Log-Hazard Estimators,” SIAM Journal of Scientific and Statistical Computation, 9, 363-379.
16. Ruppert, D., and Carroll, R. J. (2000), “Spatially-Adaptive Penalties for Spline Fitting,” Australian and New Zealand Journal of Statistics, 42, 205-223.
17. Stone, C. J., Hansen, M., Kooperberg, C., and Truong, Y. K. (1997), “Polynomial Splines and Their Tensor Products in Extended Linear Modeling” (with discussion), The Annals of Statistics, 25, 1371-1470.
18. Ullah, A. (1985), “Specification Analysis of Econometric Models,” Journal of Quantitative Economics, 2, 187-209.
19. Wahba, G. (1990), Spline Models for Observational Data, Philadelphia: Society for Industrial and Applied Mathematics.
20. Wand, M. P. (2000), “A Comparison of Regression Spline Smoothing Procedures,” Computational Statistics, 15, 443-462.