The QUANTREG Procedure

References

  • Abreveya, J. (2001). “The Effects of Demographics and Maternal Behavior on the Distribution of Birth Outcomes.” Journal of Economics 26:247–257.

  • Barro, R., and Lee, J. W. (1994). “Data Set for a Panel of 138 Countries.” Discussion paper, National Bureau of Econometric Research. http://admin.nber.org/pub/barro.lee/readme.txt.

  • Barrodale, I., and Roberts, F. D. K. (1973). “An Improved Algorithm for Discrete $l_1$ Linear Approximation.” SIAM Journal on Numerical Analysis 10:839–848.

  • Bassett, G. W., and Koenker, R. (1982). “An Empirical Quantile Function for Linear Models with iid Errors.” Journal of the American Statistical Association 77:401–415.

  • Cade, B. S., and Noon, B. R. (2003). “A Gentle Introduction to Quantile Regression for Ecologists.” Frontiers in Ecology and the Environment 1:412–420.

  • Chen, C. (2004). “An Adaptive Algorithm for Quantile Regression.” In Theory and Applications of Recent Robust Methods, edited by M. Hubert, G. Pison, A. Struyf, and S. V. Aels, 39–48. Basel: Birkhäuser.

  • Chen, C. (2005). “Growth Charts of Body Mass Index (BMI) with Quantile Regression.” In Proceedings of 2005 International Conference on Algorithmic Mathematics and Computer Science, edited by H. R. Arabnia, and I. A. Ajwa, 114–120. Bogart, GA: CSREA Press.

  • Chen, C. (2007). “A Finite Smoothing Algorithm for Quantile Regression.” Journal of Computational and Graphical Statistics 16:136–164.

  • Chock, D. P., Winkler, S. L., and Chen, C. (2000). “A Study of the Association between Daily Mortality and Ambient Air Pollutant Concentrations in Pittsburgh, Pennsylvania.” Journal of the Air and Waste Management Association 50:1481–1500.

  • Dunham, J. B., Cade, B. S., and Terrell, J. W. (2002). “Influences of Spatial and Temporal Variation on Fish-Habitat Relationships Defined by Regression Quantiles.” Transactions of the American Fisheries Society 131:86–98.

  • Gutenbrunner, C., and Jureckova, J. (1992). “Regression Rank Scores and Regression Quantiles.” Annals of Statistics 20:305–330.

  • Gutenbrunner, C., Jureckova, J., Koenker, R., and Portnoy, S. (1993). “Tests of Linear Hypotheses Based on Regression Rank Scores.” Journal of Nonparametric Statistics 2:307–331.

  • He, X., and Hu, F. (2002). “Markov Chain Marginal Bootstrap.” Journal of the American Statistical Association 97:783–795.

  • Huber, P. J. (1981). Robust Statistics. New York: John Wiley & Sons.

  • Karmarkar, N. (1984). “A New Polynomial-Time Algorithm for Linear Programming.” Combinatorica 4:373–395.

  • Koenker, R. (1994). “Confidence Intervals for Regression Quantiles.” In Asymptotic Statistics, edited by P. Mandl, and M. Huskova, 349–359. New York: Springer-Verlag.

  • Koenker, R. (2005). Quantile Regression. New York: Cambridge University Press.

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

  • Koenker, R., and Bassett, G. W. (1982a). “Robust Tests for Heteroscedasticity Based on Regression Quantiles.” Econometrica 50:43–61.

  • Koenker, R., and Bassett, G. W. (1982b). “Tests of Linear Hypotheses and $l_1$ Estimation.” Econometrica 50:1577–1583.

  • Koenker, R., and d’Orey, V. (1994). “Remark AS R92: A Remark on Algorithm AS 229: Computing Dual Regression Quantiles and Regression Rank Scores.” Journal of the Royal Statistical Society, Series C 43:410–414.

  • Koenker, R., and Hallock, K. (2001). “Quantile Regression: An Introduction.” Journal of Economic Perspectives 15:143–156.

  • Koenker, R., and Machado, A. F. (1999). “Goodness of Fit and Related Inference Processes for Quantile Regression.” Journal of the American Statistical Association 94:1296–1310.

  • Koenker, R., and Zhao, Q. (1994). “L-Estimation for Linear Heteroscedastic Models.” Journal of Nonparametric Statistics 3:223–235.

  • Kuczmarski, R. J., Ogden, C. L., and Guo, S. S. (2002). “2000 CDC Growth Charts for the United States: Methods and Development.” Vital and Health Statistics 11:1–190.

  • Lustig, I. J., Marsten, R. E., and Shanno, D. F. (1992). “On Implementing Mehrotra’s Predictor-Corrector Interior-Point Method for Linear Programming.” SIAM Journal on Optimization 2:435–449.

  • Madsen, K., and Nielsen, H. B. (1993). “A Finite Smoothing Algorithm for Linear $L_1$ Estimation.” SIAM Journal on Optimization 3:223–235.

  • Parzen, M. I., Wei, L. J., and Ying, Z. (1994). “A Resampling Method Based on Pivotal Estimating Functions.” Biometrika 81:341–350.

  • Portnoy, S., and Koenker, R. (1997). “The Gaussian Hare and the Laplacian Tortoise: Computation of Squared-Error vs. Absolute-Error Estimators.” Statistical Science 12:279–300.

  • Roos, C., Terlaky, T., and Vial, J. (1997). Theory and Algorithms for Linear Optimization. Chichester, UK: John Wiley & Sons.

  • Rousseeuw, P. J., and Van Driessen, K. (1999). “A Fast Algorithm for the Minimum Covariance Determinant Estimator.” Technometrics 41:212–223.

  • Yu, K., Lu, Z., and Stabder, J. (2003). “Quantile Regression: Application and Current Research Areas.” The Statistician 52:331–350.