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Generalized regression trees. (English) Zbl 0825.62610
Summary: Trees are powerful tools to describe and organize knowledge. As a method of data analysis, trees are used in many disciplines to structure a priori knowledge as well as to formulate and test hypotheses. The author outlines in this paper a general approach to tree-growing whose goal is to predict a parameter of a statistical model, i.e., to develop a general framework for constructing regression trees from data in the context of a generalized regression model (GLIM). Problems of overfit bias and suggestions for further work are discussed.

MSC:
62J12 Generalized linear models (logistic models)
65C99 Probabilistic methods, stochastic differential equations
62P10 Applications of statistics to biology and medical sciences; meta analysis
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