QuantileRegressionBiblio.pdf
©2014, Timothy, G. Gregoire, Yale University
Last Revised: December 2014
Quantile Regression (65 entries)
1936-Present
1. Misc. 1 Applied Statistics Off print paper.
2. Misc. 2 Lecture slides “Quantile Regression Methods for Modeling CD4 Cell Count
Trajectory among HIV –infected men (or women) on Long Term Highly Active
Antiretroviral Therapy”.
3. Misc. 3 He, X. and Wei, Y. (2005) “Tutorial on Quantile Regression”.
4. Misc. 4 SAS/STAT ® 9.1 Experimental QUANTREG Procedure for Windows.
5. Misc. 5 Lecture Note.
6. Misc. 6 Empirical Economics.
7. Thompson, W.R. (1936) “On confidence ranges for the Median and others expectation
distributions for Populations of unknown distribution form”. Annals of Mathematical
Statistics 7(3): 122-128.
8. Lloyd, E.H. (1952) “Least – Squares estimation of location and scale parameters using
order statistics”. Biometrika 39 (1/2): 88 – 95.
9. Woodruff, R.S. (1952) “Confidence intervals for Medians and other position measures”.
American Statistical Association Journal: 635 – 646.
10. Koenker, R. and Bassett, G. (1978) “Regression Quantiles”. Econometrika, 46(1): 33- 50.
11. Harrell, F.E. and Davis, C.E. (1982) “A new distribution –free quantile estimator”.
Biometrika 69(3): 635 – 640.
12. Hassanein, K.M., Saleh, A.K.M. E. and Brown, E.F. (1986) “Best Linear Unbiased
Estimators for Normal Distribution Quantiles for Sample Sizes up to 20”. IEEE
Transactions on Reliability 35 (3): 327 – 329.
13. Koenker. R.W. and Orey, V.D. (1987) “Algorithm AS 229: Computing Regression
Quantiles”. Applied Statistics 36(3): 383 – 393.
14. Portnoy, S. (1988) “Regression Quantile Diagnostics for Multiple Outliers”. Directions in
Robust Statistics and Diagnostics II (34): 65 – 113.
15. Parrish, R.S. (1990) “The Consultant’s Forum: Comparison of Quantile Estimators in
Normal Sampling”. Biometrics 46: 247 – 257.
QuantileRegressionBiblio.pdf
©2014, Timothy, G. Gregoire, Yale University
16. Sheather, S.J. and Marron, J.S. (1990) “Kernel Quantile Estimation”. Journal of the
American Association 85 (410): 410 – 416.
17. Dielman, T. Lowry, C .,Pfaffenberger, and Neeley, M. J. . 1994. A comparison of
quantile estimators. Communications in Statistics – Simulation and Computation. 23(2)
355-371.
18. Koenker, R. Ng, P. and Portnoy, S. (1994) “Quantile Smoothing Splines”. Biometrika
81(4): 673 – 680.
19. Kaiser, M.S., Speckman, P.L. and Jones, J.R. (1994) “Statistical models for limiting
nutrient relations in inland waters”. Journal of the American Statistical Association
89(426): 410 – 423.
20. Rosenbaum, P.R. (1995) “Quantiles in Nonrandom Samples and Observational Studies”.
Journal of the American Statistical Association 90(432): 1424 – 1431.
21. Hyndman, R.J. and Fan, Y. (1996) “Statistical Computing: Sample Quantiles in
Statistical Packages”. Journal of the American Statistical Association 50(4): 361 – 365.
22. Koenker, R. and Park, B. J. (1996) “An interior point algorithm for nonlinear quantile
regression.” Journal of Econometrics 71: 265-283.
23. Yu, K. and Jones, M.C. (1998) “Local Linear Quantile Regression”. Journal of the
American Statistical Association 93(441): 228 – 237.
24. Cade, B.S. and Guo, Q. (2000) “Estimating effects of constraints on plant performance
with regression quantiles”. OIKOS 91 (2): 245-254.
25. Koenker, R. (2000) “Galton, Edgeworth, and prospects for quantile regression in
econometrics”. Journal of Econometrics 95: 347 – 374.
26. Machado, J.A.F. and Mata, J. (2000) “Box – Cox Quantile Regression and the
Distribution of Firm Sizes”. Journal of Applied Econometrics 15: 253 – 274.
27. Koenker, R. and Hallock, K.F. (2001) “Quantile Regression”. Journal of Economic
Perspectives 15(4): 143 – 156.
28. Price, R.M. and Bonett, D.G. (2001) “Estimating the Variance of the Sample Median”.
Journal of Statistical Computation and simulation 68: 295 – 305.
29. Barnett, V. and Bown, M. (2002) “Best Linear unbiased quantile estimators for
environmental standards”. Environmetrics 13: 295 – 310.
QuantileRegressionBiblio.pdf
©2014, Timothy, G. Gregoire, Yale University
30. Bonett, D.G. and Price, R.M. (2002) “Statistical Inference for a Linear Function of
Medians: Confidence Intervals, Hypothesis Testing and Sample Size requirements”.
Psychological Methods, 7 (3): 370 – 383.
31. Cade, B.S. and Noon, B.R. (2003) “A gentle introduction to quantile regression for
ecologists”. Frontiers in Ecology and the Environment 1(8): 412- 420.
32. He, X. and Zhu, L.X. (2003) “A Lack-of-fit Test for Quantile Regression”. Journal of the
American Statistical Association 98 (464): 1013 – 1022.
33. Yu, Keming., Lu, Z. and Stander, J. (2003) “Quantile regression: appplications and
current research areas”. The Statistician 52(3): 331 – 350.
34. He, X., Fu, B. and Fung, W.K. (2003) “Median regression for longitudinal data”.
Statistics in Medicine 22: 3655 – 3669.
35. Honda, T. (2004) “Quantile regression in varying coefficient models”. Journal of
Statistical Planning and Inference 121: 113 – 125.
36. Koenker, R. (2004) “Quantile regression for longitudinal data”. Journal of Multivariate
Analysis 91: 74 – 89.
37. Gannoun, A., Saracco, J., Yuan, A. and Bonney, G.E. (2005) “Non – parametric Quantile
Regression with Censored Data”. Journal of Scandinavian Statistics 32: 527 – 550.
38. Horowitz, J.L. and Lee, S. (2005) “Nonparametric Estimation of an Additive Quantile
Reression Model”. Journal of the American Statistical Association 100(472): 1238.
39. Whittaker, J., Whitehead. C. and Somers, M. (2005) “The neglog transformation and
quantile regression for the analysis of a large credit scoring database”. Applied Statistics
54(5): 863 – 878.
40. Angrist, J., Chernozhukov, V. and Val, I.F. (2006) “Quantile Regression under
Misspecification, with an Application to the U.S. Wage Structure”. Econometrika 74:
539.
41. Cade,B.S. and Richards, J.D. (2006) “A Permutation Test for Quantile Regression”.
Journal of Agricultural, Biological and Environmental Statistics 11(1): 106 -126.
42. Chiang, Y.C., Chen, L.A. and Chueh, H. (2006) “Symmetric Quantiles and their
Applications”. Journal of Applied Statistics 33 (8): 807- 817.
43. Machado, J.A.F. and Silva, J.M.C.S. (2006) “Quantiles for Counts”. Journal of the
American Statistical Association: 1-12.
QuantileRegressionBiblio.pdf
©2014, Timothy, G. Gregoire, Yale University
44. Tian, M. (2006) “A Quantile Regression Analysis of Family Background Factor Effects
on Mathematical Achievement”. Journal of Data Science 4: 461 – 478.
45. Evans, A.M. and Gregiore, T.G. (2007) “A geographically variable model of hemlock
woolly adelgid spread”. Biological Invasions 9: 369 – 382.
46. Chakraborti, S. and Li.J. (2007) “Confidence Interval Estimation of a Normal Percentile”.
American Statistical Association 61(4): 331-336.
47. Fitzmaurice, G.M., Lipsitz, S.R. and Parzen, M. (2007) “Approximate Median
Regression via the Box-Cox Transformation”. The American Statistician 61(3): 233 –
238.
48. Geract, M. and Bottai, M. (2007) “Quantile Regression for Longitudinal data using the
asymmetric Laplace distribution”. Biostatistics 8(1): 140-154.
49. Gilchrist, W. (2008) “Regression Revisited”. International Statistical Review 76(3):401418.
50. Larocque,D. and Randles, R.H. (2008) “Confidence Intervals for a Discrete Population
Median”. The American Statistician 62(1): 32 -39.
51. Landaji.M., Andres, J.D. and Lorca, P. (2008) “Measuring form performance by using
linear and non-parametric quantile regressions”. Journal of Applied Statistics 57(2): 227
– 250.
52. Wei, Y. and He, X. (2008) “Conditional Growth Charts”. Journal of American
Association 103(481): 397 – 409.
53. Chen, X., Koenker, R. and Xiao, Z. (2009) “Copula-based nonlinear quantile
autoregression”. Econometrics Journal 12: s50 – s67.
54. Wang, H.J. and Wang, L. (2009) “Locally Weighted Censored Quantile Regression”.
Journal of the American Statistical Association 104(487): 1117 - 1143.
55. Evans, A.M. and Finkral, A.J. (2010) “A new look at spread rates of Exotic Diseases in
North American Forests”. Forest Science 56(5): 453-459.
56. Tarr, G. (2010) “Small sample performance of quantile regression confidence intervals”.
Journal of Statistical Computation and Simulation 80(1): 81 – 94.
57. Haupt, H., Kagerer, K. and Schn urbus, J. (2011) “Cross-validating fit and predictive
accuracy of nonlinear quantile regression”. Journal of Applied Statistics: 1-16.
QuantileRegressionBiblio.pdf
©2014, Timothy, G. Gregoire, Yale University
58. Slavati, N., Ranalli, M.G. and Pratesi, M. (2011) “Small area estimation of the mean
using non-parametric M-quantile regression: a comparison when a linear mixed model
does not hold”. Journal of Statistical Computation and Simulation 81(8): 945 – 964.
59. Kostov, P. (2012) “Empirical likelihood estimation of the spatial quantile regression”.
Journal of Geographical Systems: 1-23.
60. Muggeo, V.M.R., Sciandra, M. and Augugliaro, L. (2012) “Quantile regression via
iterative least squares computations”. Journal of Statistical Computation and Simulation
82 (11): 1557 – 1569.
61. Wilcox, R. R. and Erceg-Hurn, D. M. 2012. Comparing two dependent groups via
quantiles. Journal of Applied Statistics 39(12) 2655-2664.
62. Noh, H., Ghouch, A.E. and Keilegom, I.V. (2012) “Quality of Fit measures in the
Framework of Quantile Regression”. Journal of Scandinavian Statistics: 40(1): 105 --118
63. Noufaily, A, & Jones, M. C. 2013. Parametric quantile regression based on the
generalized gamma distribution. Applied Statistics 62(5) 723 – 740.
64. Alhamzawi, R. 2014. Model selection in quantile regression models. Journal of Applied
Statistics 42(2) 445 – 458. doi 10.1080/02664763.2014.959905
65. Shen, P-S. 2014. A weighted quantile regression for left-truncated and right-truncated
data. Journal of Statistical Computation and Simulation. 84(3) 596-604. doi
10.1080/00949655.2012.721117