1 |
Boggs, P. T. and Rogers, J. E. (1990). Orthogonal distance regression. Contemporary Mathematics, 112, 183-194.
DOI
|
2 |
Boggs, P. T., Byrd, R. H. and Schnabel, R. B. (1987). A stable and efficient algorithm for nonlinear orthogonal distance regression. SIAM Journal on Scientific Computing, 8, 105-1078.
|
3 |
Chesher, A. (2001). Parameter approximations for quantile regressions with measurement error, Working Paper CWP02/01, Department of Economics, University College London, London, UK.
|
4 |
Fuller, W. A. (1987). Measurement error models, Wiley, New York.
|
5 |
He, X. and Liang, H. (2000). Quantile regression estimates for a class of linear and partially linear errors-in-variables models. Statistica Sinica, 10, 129-140.
|
6 |
Hu, Y. and Schennach, S. M. (2008). Identification and estimation of nonclassical nonlinear errors-in-variables models with continuous distributions using instruments. Econometrica, 76, 195-216.
DOI
ScienceOn
|
7 |
Koenker, R. and Bassett, G. (1978). Regression quantile. Econometrica, 46, 33-50.
DOI
ScienceOn
|
8 |
Hwang, C. (2010). M-quantile regression using kernel machine technique. Journal of the Korean Data & Information Science Society, 21, 973-981.
과학기술학회마을
|
9 |
Ioannidesa, D. A. and Matzner-Lober, E. (2009). Regression quantiles with errors-invariables. Journal of Nonparametric Statistics, 21, 1003-1015.
DOI
ScienceOn
|
10 |
Koenker, R. (2005). Quantile regression, Cambridge University Press, Cambridge.
|
11 |
Koenker, R., Ng, P. and Portnoy, S. (1994). Quantile smoothing splines, Biometrika, 81, 673-680.
DOI
ScienceOn
|
12 |
Lee, S. (2012). Forecasting value-at-risk by encompassing CAViaR models via information criteria. Journal of the Korean Data & Information Science Society, 24, 1531-1541.
과학기술학회마을
DOI
ScienceOn
|
13 |
Ma, Y. and Yin, G. (2011). Censored quantile regression with covariate measurement errors. Statistica Sinica, 21, 949-971.
DOI
|
14 |
Schennach, S. M. (2008). Quantile regression with mismeasured covariates. Econometric Theory, 24, 1010-1043.
|
15 |
Shim, J. and Lee, J. (2010). Restricted support vector regression without crossing. Journal of the Korean Data & Information Science Society, 21, 1319-1325.
과학기술학회마을
|
16 |
Wang, H. J., Stefanski, L. A. and Zhu, Z. (2012). Corrected-loss estimation for quantile regression with covariate measurement errors. Biometrika, 99, 405-421.
DOI
ScienceOn
|
17 |
Takeuchi, I., Le, Q. V., Sears, T. and Smola, A. J. (2006). Nonparametric quantile regression. Journal of Machine Learning Research, 7, 1231-1264.
|
18 |
Van Gorp, J., Schoukens, J. and Pintelon, R. (2000). Learning neural networks with noisy inputs using the errors-in-variables approach. IEEE Transactions on Neural Networks, 11, 402-414.
DOI
ScienceOn
|
19 |
Vapnik, V. N. (1998). Statistical learning theory, John Wiley, New York.
|
20 |
Wei, Y. and Carroll, R. J. (2009). Quantile regression with measurement error. Journal of the American Statistical Association, 104, 1129-1143.
DOI
ScienceOn
|
21 |
Yu, K., Lu, Z. and Stander, J. (2003). Quantile regression: Applications and current research area. The Statistician, 52, 331-350.
|