Chaudhuri, Probal ; Lo, Wen-Da ; Loh, Wei-Yin ; Yang, Ching-Ching (1995) Generalized regression trees Statistica Sinica, 5 . pp. 641-666. ISSN 1017-0405
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Official URL: http://www3.stat.sinica.edu.tw/statistica/j5n2/j5n...
Abstract
A method of generalized regression that blends tree-structured nonparametric regression and adaptive recursive partitioning with maximum likelihood estimation is studied. The function estimate is a piecewise polynomial, with the pieces determined by the terminal nodes of a binary decision tree. The decision tree is constructed by recursively partitioning the data according to the signs of the residuals from a model fitted by maximum likelihood to each node. Algorithms for tree-structured Poisson and logistic regression and examples to illustrate them are given. Large-sample properties of the estimates are derived under appropriate regularity conditions.
| Item Type: | Article |
|---|---|
| Source: | Copyright of this article belongs to Academia Sinica. |
| Keywords: | Anscombe Residual; Consistency; Generalized Linear Model; Maximum Likelihood; Pseudo Residual; Recursive Partitioning; Vapnik-chervonenkis Class |
| ID Code: | 74628 |
| Deposited On: | 17 Dec 2011 10:08 |
| Last Modified: | 17 Dec 2011 10:08 |
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