The ADAPTIVEREG Procedure

Asuncion, A., and Newman, D. J. (2007). “UCI Machine Learning Repository.” http://archive.ics.uci.edu/ml/.

Bellman, R. E. (1961). Adaptive Control Processes. Princeton, NJ: Princeton University Press.

Bowman, A. W., and Azzalini, A. (1997). Applied Smoothing Techniques for Data Analysis. New York: Oxford University Press.

Breiman, L., Friedman, J., Olshen, R. A., and Stone, C. J. (1984). Classification and Regression Trees. Belmont, CA: Wadsworth.

Buja, A., Duffy, D., Hastie, T. J., and Tibshirani, R. (1991). “Discussion: Multivariate Adaptive Regression Splines.” Annals of Statistics 19:93–99.

Craven, P., and Wahba, G. (1979). “Smoothing Noisy Data with Spline Functions.” Numerical Mathematics 31:377–403.

Friedman, J. H. (1991a). Estimating Functions of Mixed Ordinal and Categorical Variables Using Adaptive Splines. Technical report, Stanford University.

Friedman, J. H. (1991b). “Multivariate Adaptive Regression Splines.” Annals of Statistics 19:1–67.

Friedman, J. H. (1993). Fast MARS. Technical report, Stanford University.

Gu, C., Bates, D. M., Chen, Z., and Wahba, G. (1990). “The Computation of GCV Function through Householder Tridiagonalization with Application to the Fitting of Interaction Splines Models.” SIAM Journal on Matrix Analysis and Applications 10:457–480.

Hastie, T. J., and Tibshirani, R. J. (1990). Generalized Additive Models. New York: Chapman & Hall.

Hastie, T. J., Tibshirani, R. J., and Friedman, J. H. (2001). The Elements of Statistical Learning: Data Mining, Inference, and Prediction. New York: Springer-Verlag.

Owen, A. (1991). “Discussion of 'Multivariate Adaptive Regression Splines' by J. H. Friedman.” Annals of Statistics 19:102–112.

Smith, P. L. (1982). Curve Fitting and Modeling with Splines Using Statistical Variable Selection Techniques. Technical report, NASA Langley Research Center.