The “progressive mixture” estimator for regression trees

Gilles Blanchard

Annales de l'I.H.P. Probabilités et statistiques (1999)

  • Volume: 35, Issue: 6, page 793-820
  • ISSN: 0246-0203

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Blanchard, Gilles. "The “progressive mixture” estimator for regression trees." Annales de l'I.H.P. Probabilités et statistiques 35.6 (1999): 793-820. <http://eudml.org/doc/77646>.

@article{Blanchard1999,
author = {Blanchard, Gilles},
journal = {Annales de l'I.H.P. Probabilités et statistiques},
language = {eng},
number = {6},
pages = {793-820},
publisher = {Gauthier-Villars},
title = {The “progressive mixture” estimator for regression trees},
url = {http://eudml.org/doc/77646},
volume = {35},
year = {1999},
}

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
PB - Gauthier-Villars
VL - 35
IS - 6
SP - 793
EP - 820
LA - eng
UR - http://eudml.org/doc/77646
ER -

References

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  3. [3] A. Barron and Y. Yang, Information theoretic determination of minimax rates of convergence, Department of Statistics, Yale University, 1997. Zbl0978.62008
  4. [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. [5] L. Birgé, Approximation dans les espaces métriques et théorie de l'approximation, Z. Wahrscheinlichkeitstheor. Verw. Geb.65 (1983) 181-237. Zbl0506.62026MR722129
  6. [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. [7] H. Chipman, E.I. George and E. Mcculloch, Bayesian CART model search, JASA93 (1998) 935-947. 
  8. [8] T.M. Cover and J.A. Thomas, Elements of Information Theory, Wiley Series in Telecommunications, Wiley, New York, 1991. Zbl0762.94001MR1122806
  9. [9] L. Devroye and L. Györfi, Nonparametric Density Estimation: The L1 View, Wiley, New York, 1985. Zbl0546.62015MR780746
  10. [10] D. Helmbold and R. Shapire, Predicting nearly as well as the best pruning of a decision tree, Machine Learning27 (1997) 51-68. 
  11. [11] F.M.J. Willems, Y.M. Shtarkov and T.J. Tjalkens, The context-tree weighting method: basic properties, IEEE Trans. Inform. Theory41 (3) (1995) 653-664. Zbl0837.94011
  12. [12] F.M.J. Willems, Y.M. Shtarkov and T.J. Tjalkens, Context weighting for general finite-context sources, IEEE Trans. Inform. Theory42 (5) (1996) 1514- 1520. Zbl0860.94016

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