The GLMSELECT Procedure

Breiman, L. (1992), “The Little Bootstrap and Other Methods for Dimensionality Selection in Regression: X-Fixed Prediction Error,” Journal of the American Statistical Association, 87, 738–754.

Burnham, K. P. and Anderson, D. R. (2002), Model Selection and Multimodel Inference, 2nd Edition, New York: Springer-Verlag.

Darlington, R. B. (1968), “Multiple Regression in Psychological Research and Practice,” Psychological Bulletin, 69, 161–182.

Donoho, D. L. and Johnstone, I. M. (1994), “Ideal Spatial Adaptation via Wavelet Shrinkage,” Biometrika, 81, 425–455.

Draper, N. R., Guttman, I., and Kanemasu, H. (1971), “The Distribution of Certain Regression Statistics,” Biometrika, 58, 295–298.

Efron, B., Hastie, T. J., Johnstone, I. M., and Tibshirani, R. (2004), “Least Angle Regression (with Discussion),” Annals of Statistics, 32, 407–499.

Efron, B. and Tibshirani, R. J. (1993), An Introduction to the Bootstrap, New York: Chapman & Hall.

Eilers, P. H. C. and Marx, B. D. (1996), “Flexible Smoothing with B-Splines and Penalties,” Statistical Science, 11, 89–121, with discussion.

Foster, D. P. and Stine, R. A. (2004), “Variable Selection in Data Mining: Building a Predictive Model for Bankruptcy,” Journal of the American Statistical Association, 99, 303–313.

Harrell, F. E. (2001), Regression Modeling Strategies, New York: Springer-Verlag.

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

Hocking, R. R. (1976), “The Analysis and Selection of Variables in a Linear Regression,” Biometrics, 32, 1–50.

Hurvich, C. M., Simonoff, J. S., and Tsai, C.-L. (1998), “Smoothing Parameter Selection in Nonparametric Regression Using an Improved Akaike Information Criterion,” Journal of the Royal Statistical Society, Series B, 60, 271–293.

Hurvich, C. M. and Tsai, C.-L. (1989), “Regression and Time Series Model Selection in Small Samples,” Biometrika, 76, 297–307.

Judge, G. G., Griffiths, W. E., Hill, R. C., Lütkepohl, H., and Lee, T.-C. (1985), The Theory and Practice of Econometrics, 2nd Edition, New York: John Wiley & Sons.

Mallows, C. L. (1967), “Choosing a Subset Regression,” Bell Telephone Laboratories.

Mallows, C. L. (1973), “Some Comments on ,” Technometrics, 15, 661–675.

Miller, A. J. (2002), Subset Selection in Regression, volume 95 of Monographs on Statistics and Applied Probability, 2nd Edition, Boca Raton, FL: Chapman & Hall/CRC.

Osborne, M. R., Presnell, B., and Turlach, B. A. (2000), “A New Approach to Variable Selection in Least Squares Problems,” IMA Journal of Numerical Analysis, 20, 389–404.

Raftery, A. E., Madigan, D., and Hoeting, J. A. (1997), “Bayesian Model Averaging for Linear Regression Models,” Journal of the American Statistical Association, 92, 179–191.

Reichler, J. L., ed. (1987), The 1987 Baseball Encyclopedia Update, New York: Macmillan.

Sarle, W. S. (2001), “Donoho-Johnstone Benchmarks: Neural Net Results,” ftp://ftp.sas.com/pub/neural/dojo/dojo.html, accessed March 27, 2007.

Sawa, T. (1978), “Information Criteria for Discriminating among Alternative Regression Models,” Econometrica, 46, 1273–1282.

Schwarz, G. (1978), “Estimating the Dimension of a Model,” Annals of Statistics, 6, 461–464.

Tibshirani, R. (1996), “Regression Shrinkage and Selection via the Lasso,” Journal of the Royal Statistical Society, Series B, 58, 267–288.

Time Inc. (1987), “What They Make,” Sports Illustrated, April, 54–81.

Zou, H. (2006), “The Adaptive Lasso and Its Oracle Properties,” Journal of the American Statistical Association, 101, 1418–1429.

Zou, H. and Hastie, T. (2005), “Regularization and Variable Selection via the Elastic Net,” Journal of the Royal Statistical Society, Series B, 67, 301–320.