The QUANTSELECT Procedure (Experimental)


  • Akaike, H. (1981), “Likelihood of a Model and Information Criteria,” Journal of Econometrics, 16, 3–14.

  • Belloni, A. and Chernozhukov, V. (2011), “L1-Penalized Quantile Regression in High-Dimensional Sparse Models,” Annals of Statistics, 39, 82–130.

  • Chernozhukov, V. and Hansen, C. (2008), “Instrumental Variable Quantile Regression: A Robust Inference Approach,” Journal of Econometrics, 142, 379–398.

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

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

  • 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.

  • Koenker, R. and Bassett, G. W. (1978), “Regression Quantiles,” Econometrica, 46, 33–50.

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

  • 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.

  • Wu, Y. and Liu, Y. (2009), “Variable Selection in Quantile Regression,” Statistica Sinica, 19, 801–817.

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