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**Tree-structured regression and the differentiation of integrals.**
*(English)*
Zbl 1122.62027

Summary: This paper provides answers to questions regarding the almost sure limiting behavior of rooted, binary tree-structured rules for regression. Examples show that questions raised by L. Gordon and the present author [J. Multivariate Anal. 15, 147–163 (1984; Zbl 0542.62032)] have negative answers. For these examples of regression functions and sequences of their associated binary tree-structured approximations, for all regression functions except those in a set of the first category, almost sure consistency fails dramatically on events of full probability. One consequence is that almost sure consistency of binary tree-structured rules such as classification and regression tree (CART) requires conditions beyond requiring that (1) the regression function be in \({\mathcal L}^1\), (2) partitions of a Euclidean feature space be into polytopes with sides parallel to the coordinate axes, (3) the mesh of the partitions becomes arbitrarily fine almost surely and (4) the empirical learning sample content of each polytope be “large enough.”

The material in this paper includes the solution to a problem raised by Dudley in discussions. The main results have a corollary regarding the lack of almost sure consistency of certain Bayes-risk consistent rules for classification.

The material in this paper includes the solution to a problem raised by Dudley in discussions. The main results have a corollary regarding the lack of almost sure consistency of certain Bayes-risk consistent rules for classification.

### MSC:

62G08 | Nonparametric regression and quantile regression |

60F15 | Strong limit theorems |

28A15 | Abstract differentiation theory, differentiation of set functions |

26B05 | Continuity and differentiation questions |

62C12 | Empirical decision procedures; empirical Bayes procedures |

### Citations:

Zbl 0542.62032### Software:

ElemStatLearn### References:

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