Free Access
Issue |
ESAIM: PS
Volume 18, 2014
|
|
---|---|---|
Page(s) | 483 - 502 | |
DOI | https://doi.org/10.1051/ps/2013046 | |
Published online | 10 October 2014 |
- B. Abdous, Computationally efficient classes of higher-order kernel functions. Can. J. Statist. 23 (1995) 21–27. [CrossRef] [Google Scholar]
- L. Breiman, Using adaptive bagging to debias regressions. Technical Report 547, Dpt of Statist., UC Berkeley (1999). [Google Scholar]
- L. Breiman and J. Friedman, Estimating optimal transformation for multiple regression and correlation. J. Amer. Stat. Assoc. 80 (1995) 580–598. [Google Scholar]
- P. Bühlmann and B. Yu, Boosting with the l2 loss: Regression and classification. J. Amer. Stat. Assoc. 98 (2003) 324–339. [CrossRef] [Google Scholar]
- P.-A. Cornillon, N. Hengartner and E. Matzner-Løber, Recursive bias estimation and l2 boosting. Technical report, ArXiv:0801.4629 (2008). [Google Scholar]
- P.-A. Cornillon, N. Hengartner and Matzner-Løber, ibr: Iterative Bias Reduction. CRAN (2010). http://cran.r-project.org/web/packages/ibr/index.html. [Google Scholar]
- P.-A. Cornillon, N. Hengartner, N. Jégou and Matzner-Løber, Iterative bias reduction: a comparative study. Statist. Comput. (2012). [Google Scholar]
- P. Craven and G. Wahba, Smoothing noisy data with spline functions. Numer. Math. 31 (1979) 377–403. [Google Scholar]
- M. Di Marzio and C. Taylor, On boosting kernel regression. J. Statist. Plan. Infer. 138 (2008) 2483–2498. [CrossRef] [Google Scholar]
- R. Eubank, Nonparametric regression and spline smoothing. Dekker, 2nd edition (1999). [Google Scholar]
- W. Feller, An introduction to probability and its applications, vol. 2. Wiley (1966). [Google Scholar]
- J. Friedman, Multivariate adaptive regression splines. Ann. Statist. 19 (1991) 337–407. [Google Scholar]
- J. Friedman, Greedy function approximation: A gradient boosting machine. Ann. Statist. 28 (1189–1232) (2001). [Google Scholar]
- J. Friedman and W. Stuetzle, Projection pursuit regression. J. Amer. Statist. Assoc. 76 (817–823) (1981). [Google Scholar]
- J. Friedman, T. Hastie and R. Tibshirani, Additive logistic regression: a statistical view of boosting. Ann. Statist. 28 (2000) 337–407. [Google Scholar]
- C. Gu, Smoothing spline ANOVA models. Springer (2002). [Google Scholar]
- L. Gyorfi, M. Kohler, A. Krzyzak and H. Walk, A Distribution-Free Theory of Nonparametric Regression. Springer Verlag (2002). [Google Scholar]
- D. Harrison and D. Rubinfeld, Hedonic prices and the demand for clean air. J. Environ. Econ. Manag. (1978) 81–102. [Google Scholar]
- T. Hastie and R. Tibshirani, Generalized Additive Models. Chapman & Hall (1995). [Google Scholar]
- R.A. Horn and C.R. Johnson, Matrix analysis. Cambridge (1985). [Google Scholar]
- C. Hurvich, G. Simonoff and C.L. Tsai, Smoothing parameter selection in nonparametric regression using and improved akaike information criterion. J. Roy. Stat. Soc. B 60 (1998) 271–294. [Google Scholar]
- O. Lepski, Asymptotically minimax adaptive estimation. I: upper bounds. optimally adaptive estimates. Theory Probab. Appl. 37 (1991) 682–697. [Google Scholar]
- K.-C. Li, Asymptotic optimality for Cp, CL, cross-validation and generalized cross-validation: Discrete index set. Ann. Statist. 15 (1987) 958–975. [CrossRef] [MathSciNet] [Google Scholar]
- G. Ridgeway, Additive logistic regression: a statistical view of boosting: Discussion. Ann. Statist. 28 (2000) 393–400. [Google Scholar]
- L. Schwartz, Analyse IV applications à la théorie de la mesure. Hermann (1993). [Google Scholar]
- W. Stuetzle and Y. Mittal, Some comments on the asymptotic behavior of robust smoothers, in Smoothing Techniques for Curve Estimation, edited by T. Gasser and M. Rosenblatt. Springer-Verlag (1979) 191–195. [Google Scholar]
- J. Tukey, Explanatory Data Analysis. Addison-Wesley (1977). [Google Scholar]
- F. Utreras, Convergence rates for multivariate smoothing spline functions. J. Approx. Theory (1988) 1–27. [Google Scholar]
- J. Wendelberger, Smoothing Noisy Data with Multivariate Splines and Generalized Cross-Validation. PhD thesis, University of Wisconsin (1982). [Google Scholar]
- S. Wood, Thin plate regression splines. J. R. Statist. Soc. B 65 (2003) 95–114. [Google Scholar]
- Y. Yang, Combining different procedures for adaptive regression. J. Mult. Analysis 74 (2000) 135–161. [CrossRef] [MathSciNet] [Google Scholar]
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