Adaptive quantile regression

Recent Advances and Trends in Nonparametric Statistics

Volumn , Issue , 2003, Pages 235-250

  van de Geer, S A ^a  

^a LEIDEN UNIVERSITY   (Netherlands)

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EID: 23744510605     PISSN: None     EISSN: None     Source Type: Book    
DOI: 10.1016/B978-044451378-6/50016-8     Document Type: Chapter

References (19)

1. Akaike H. Information theory and an extension of the maximum likelihood principle. Proceedings 2nd International Symposium on Information Theory 1973, 267-281. Akademia Kiado, New York. P.N. Petrov, F. Csaki (Eds.).
2. Donoho D.L., Johnstone I.M. Ideal spatial adaptation via wavelet shrinkage. Biometrika 1994, (No. 81):425-455.
3. Donoho D.L. Denoising via soft-thresholding. IEEE Transactions in Information Theory 1995, (No. 41):613-627.
4. Edmunds E., Triebel H. Entropy numbers and approximation numbers in function spaces. II. Proceedings of the London Mathematical Society 1992, 153-169. (3) No. 64.
5. Hastie T., Tibshirani R., Friedman J. The Elements of Statistical Learning. Data Mining, Inference and Prediction 2001, Springer, Budapest.
6. Koenker R., Bassett R. Regression quantiles. Econometrica 1978, (No. 46):33-50.
7. 1 Statistical Analysis and Related Methods 1992, 217-229. Elsevier, New York. Y. Dodge (Ed.).
8. Koenker R., Ng P.T., Portnoy S.L. Quantile smoothing splines. Biometrika 1994, (No. 81):673-680.
9. Massart P. Some applications of concentration inequalities to statistics. Ann. Fac. Sci. Toulouse 2000, (No. 9):245-303.
10. Ledoux M., Talagrand M. Probability in Banach Spaces, Isoperimetry and Processes 1991, Springer, Amsterdam.
11. Loubes J.-M., van de Geer S. Adaptive estimation, using soft thresholding type penalties. Statistica Neerlandica 2002, (No. 56):453-478.
12. Pinkus A. n-widths in Approxiamation Theory 1985, Springer, Berlin.
13. Portnoy S. Local asymptotics for quantile smoothing splines. Ann. Statist. 1997, (No. 25):414-434.
14. Portnoy S., Koenker R. The Guassian hare and the Laplacian tortoise: computability of squared error versus absolute-error estimators, with discussion. Stat. Science 1997, (No. 12):279-300.
15. Schwarz G. Estimating the dimension of a model. Ann. Statist. 1978, (No. 6):461-464.
16. Tibshirani R. Regression analysis and selection via the LASSO. Journal Royal Statist. Soc. B 1996, (No. 58):267-288.
17. van de Geer S. Empirical Processes in M-Estimation 2000, Cambridge University Press, New York.
18. van de Geer S. M-estimation using penalties or sieves. J. Statist. Planning Inf. 2002, (No. 108):55-69.
19. van der Vaart A.W., Wellner J.A. Weak Convergence and Empirical Processes, with Applications to Statistics 1996, Springer.