no. 6
The “progressive mixture” estimator for regression trees
Annales de l'I.H.P. Probabilités et statistiques, Tome 35 (1999) no. 6, pp. 793-820. 
@article{AIHPB_1999__35_6_793_0,
     author = {Blanchard, Gilles},
     title = {The {\textquotedblleft}progressive mixture{\textquotedblright} estimator for regression trees},
     journal = {Annales de l'I.H.P. Probabilit\'es et statistiques},
     pages = {793--820},
     publisher = {Gauthier-Villars},
     volume = {35},
     number = {6},
     year = {1999},
     mrnumber = {1725711},
     zbl = {1054.62539},
     language = {en},
     url = {http://archive.numdam.org/item/AIHPB_1999__35_6_793_0/}
}
TY  - JOUR
AU  - Blanchard, Gilles
TI  - The “progressive mixture” estimator for regression trees
JO  - Annales de l'I.H.P. Probabilités et statistiques
PY  - 1999
SP  - 793
EP  - 820
VL  - 35
IS  - 6
PB  - Gauthier-Villars
UR  - http://archive.numdam.org/item/AIHPB_1999__35_6_793_0/
LA  - en
ID  - AIHPB_1999__35_6_793_0
ER  - 
%0 Journal Article
%A Blanchard, Gilles
%T The “progressive mixture” estimator for regression trees
%J Annales de l'I.H.P. Probabilités et statistiques
%D 1999
%P 793-820
%V 35
%N 6
%I Gauthier-Villars
%U http://archive.numdam.org/item/AIHPB_1999__35_6_793_0/
%G en
%F AIHPB_1999__35_6_793_0
Blanchard, Gilles. The “progressive mixture” estimator for regression trees. Annales de l'I.H.P. Probabilités et statistiques, Tome 35 (1999) no. 6, pp. 793-820. http://archive.numdam.org/item/AIHPB_1999__35_6_793_0/

[1] Y. Amit and D. Geman, Shape quantization and recognition with randomized trees, Neural Computation 9 (1997) 1545-1588.

[2] Y. Amit, D. Geman and K. Wilder, Joint induction of shape features and tree classifiers, IEEE Trans. PAMI 19 (11) (1997) 1300-1306.

[3] A. Barron and Y. Yang, Information theoretic determination of minimax rates of convergence, Department of Statistics, Yale University, 1997.

[4] A.A. Barron, Are Bayes rules consistent in information? in: T.M. Cover and B. Gopinath (Eds.), Open Problems in Communication and Computation, Springer, Berlin, 1987, pp. 85-91.

[5] L. Birgé, Approximation dans les espaces métriques et théorie de l'approximation, Z. Wahrscheinlichkeitstheor. Verw. Geb. 65 (1983) 181-237. | MR | Zbl

[6] O. Catoni, "Universal" aggregation rules with exact bias bounds, Preprint of the Laboratoire de Probabilités et Modèles Aléatoires, Université Pierre et Marie Curie, available at http://www.proba.jussieu.fr/mathdoc/preprints/index.html#1999 (to appear in Annals of Statistics), 1999.

[7] H. Chipman, E.I. George and E. Mcculloch, Bayesian CART model search, JASA 93 (1998) 935-947.

[8] T.M. Cover and J.A. Thomas, Elements of Information Theory, Wiley Series in Telecommunications, Wiley, New York, 1991. | MR | Zbl

[9] L. Devroye and L. Györfi, Nonparametric Density Estimation: The L1 View, Wiley, New York, 1985. | MR | Zbl

[10] D. Helmbold and R. Shapire, Predicting nearly as well as the best pruning of a decision tree, Machine Learning 27 (1997) 51-68.

[11] F.M.J. Willems, Y.M. Shtarkov and T.J. Tjalkens, The context-tree weighting method: basic properties, IEEE Trans. Inform. Theory 41 (3) (1995) 653-664. | Zbl

[12] F.M.J. Willems, Y.M. Shtarkov and T.J. Tjalkens, Context weighting for general finite-context sources, IEEE Trans. Inform. Theory 42 (5) (1996) 1514- 1520. | Zbl