Quantile regression: Applications and current research areas

Journal of the Royal Statistical Society Series D: The Statistician

Volumn 52, Issue 3, 2003, Pages 331-350

  Yu, Keming ^b   Lu, Zudi ^a   Stander, Julian ^c  

^a INSTITUTE OF GEOLOGY AND GEOPHYSICS   (China)

^b UNIVERSITY OF PLYMOUTH   (United Kingdom)

^c NONE

Author keywords

Check function; Conditional distribution; Quantile; Regression fitting; Skew distribution

Indexed keywords

EID: 0142042364     PISSN: 00390526     EISSN: None     Source Type: Journal    
DOI: 10.1111/1467-9884.00363     Document Type: Review

References (39)

1. Altman, N. S. (1990) Kernel smoothing of data with correlated errors. J. Am. Statist. Ass., 85, 749-759.
2. Bailar, B. (1991) Salary survey of U.S. colleges and universities offering degrees in statistics. Amstat News, 182, 3-10.
3. Bassett, G. W. and Chen, H. (2001) Portfolio style: return-based attribution using quantile regression. Emp. Econ., 26, 293-305.
4. Besag, J. and Higdon, D. (1999) Bayesian analysis of agricultural field experiments (with discussion). J. R. Statist. Soc. B, 61, 691-746.
5. Bhattacharya, P. K. and Gangopadhyay, A. K. (1990) Kernel and nearest-neighbor estimation of a conditional quantile. Ann. Statist., 18, 1400-1415.
6. Buchinsky, M. (1995) Quantile regression, Box-Cox transformation model, and the U.S. wage structure, 1963-1987. J. Econometr., 65, 109-154.
7. Cai, Z. (2002) Regression quantiles for time series. Econometr. Theory, 18, 169-192.
8. Chaudhuri, P., Doksum, K. and Samarov, A. (1997) On average derivative quantile regression. Ann. Statist., 25, 715-744.
9. Chen, M.-H., Shao, Q.-M. and Ibrahim, J. G. (2001) Monte Carlo Methods in Bayesian Computation. New York: Springer.
10. Cole, T. J. (1988) Fitting smoothed centile curves to reference data (with discussion). J. R. Statist. Soc. A, 151, 385-418.
11. Cole, T. J. and Green, P. J. (1992) Smoothing reference centile curves: the LMS method and penalized likelihood. Statist. Med, 11, 1305-1319.
12. Crowley, J. and Hu, M. (1977) Covariance analysis of heart transplant data. J. Am. Statist. Ass., 72, 27-36.
13. Gannoun, A., Saracco, J. and Yu, K. (2003) Nonparametric prediction by conditional median and quantiles. J. Statist. Planng Inf., to be published.
14. Hall, P., Wolff, R. C. L. and Yao, Q. (1999) Methods for estimating a conditional distribution. J. Am. Statist. Ass., 94, 154-163.
15. He, X., Ng, P. and Portnoy, S. (1998) Bivariate quantile smoothing splines. J. R. Statist. Soc. B, 60, 537-550.
16. Heagerty, P. J. and Pepe, M. S. (1999) Semiparametric estimation of regression quantiles with application to standardizing weight for height and age in US children. Appl. Statist, 48, 533-551.
17. Hendricks, W. and Koenker, R. (1992) Hierarchical spline models for conditional quantiles and the demand for electricity. J. Am. Statist. Ass., 93, 58-68.
18. Hull, J. and White, A. (1998) Value at risk when daily changes in market variables are not normally distributed. J. Deriv., 5, 9-19.
19. Isaacs, D., Altman, D. G., Tidmarsh, C. E., Valman, H. B. and Webster, A. D. B. (1983) Serum Immunoglobin concentrations in preschool children measured by laser nephelometry: reference ranges for IgG, IgA and IgM. J. Clin. Path., 36, 1193-1196.
20. Koenker, R. W and D'Orey, V. (1987) Algorithm AS 229: Computing regression quantiles. Appl. Statist., 36, 383-393.
21. Koenker, R. and Geling, R. (2001) Reappraising medfly longevity: a quantile regression survival analysis. J. Am. Statist. Ass., 96, 458-468.
22. Koenker, R. and Machado, J. (1999) Goodness of fit and related inference processes for quantile regression. J. Am. Statist. Ass., 94, 1296-1309.
23. Koenker, R. and Park, B. J. (1996) An interior point algorithm for nonlinear quantile regression. J. Econometr., 71, 265-283.
24. 1 Statistical Analysis and Related Methods (ed. Y. Dodge), pp. 217-229. Amsterdam: Elsevier.
25. Kottas, A. and Gelfand, A. E. (2001) Bayesian semiparametric median regression model. J. Am. Statist. Ass., 96, 1458-1468.
26. Lauridsen, S. (2000) Estimation of value of risk by extreme value methods. Extremes, 3, 107-144.
27. Lipsitz, S. R., Fitzmaurice, G. M., Molenberghs, G. and Zhao, L. P. (1997) Quantile regression methods for longitudinal data with drop-outs: application to CD4 cell counts of patients infected with the human immunodeficiency virus. Appl. Statist., 46, 463-476.
28. Pandey, G. R. and Nguyen, V. T. (1999) A comparative study of regression based methods in regional flood frequency analysis. J. Hydrol., 225, 92-101.
29. Portnoy, S. and Koenker, R. (1997) The Gaussian hare and the Laplacian tortoise: computability of squared-error versus absolute-error estimators (with discussion). Statist. Sci., 12, 279-300.
30. Royston, P. and Altman, D. G. (1994) Regression using fractional polynomials of continuous covariates: parsimonious parametric modelling (with discussion). Appl. Statist., 43, 429-467.
31. Royston, P. and Wright, E. M. (2000) Goodness-of-fit statistics for age-specific reference intervals. Statist. Med., 19, 2943-2962.
32. Ruppert, D., Sheather, S. J. and Wand, M. P. (1995) An effective bandwidth selector for local least squares regression. J. Am. Statist. Ass., 90, 1257-1270.
33. Silverman, B. W. (1985) Some aspects of the spline smoothing approach to non-parametric regression curve fitting (with discussion). J. R. Statist. Soc. B, 47, 1-52.
34. Taylor, J. (1999) A quantile regression approach to estimating the distribution of multiperiod returns. J. Deriv., 24, 64-78.
35. Yang, S. (1999) Censored median regression using weighted empirical survival and hazard functions. J. Am. Statist. Ass., 94, 137-145.
36. Yu, K. (1999) Smoothing regression quantile by combining k-NN with local linear fitting. Statist. Sin., 9, 759-771.
37. Yu, K. (2002) Quantile regression using RJMCMC algorithm. Comput. Statist. Data Anal, 40, 303-315.
38. Yu, K. and Jones, M. C. (1998) Local linear regression quantile estimation. J. Am. Statist. Ass., 93, 228-238.
39. Yu, K. and Moyeed, R. A. (2001) Bayesian quantile regression. Statist. Probab. Lett., 54, 437-447.