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Regression Trees

  • Brian Everitt
  • Sophia Rabe-Hesketh
Part of the Statistics for Biology and Health book series (SBH)

Abstract

Previous chapters have dealt with a number of regression type models, linear and multiple regression (Chapters 4 and 8), generalized linear models (Chapters 9 and 10), mixed-effects regression for longitudinal data (Chapters 11 and 12) and generalized additive and nonlinear models (Chapters 13 and 14). These parametric regression methods are widely used, but they may not give faithful data descriptions when the assumptions on which they are based are not met, or in the presence of higher order interactions among some of the explanatory variables.

Keywords

Root Node Terminal Node Split Function Random Number Seed Daughter Node 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer Science+Business Media New York 2001

Authors and Affiliations

  • Brian Everitt
    • 1
  • Sophia Rabe-Hesketh
    • 1
  1. 1.Biostatistics and Computing DepartmentInstitute of PsychiatryLondonUK

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