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Regression Trees and Adaptive Splines for a Continuous Response

  • Heping Zhang
  • Burton H. Singer
Chapter
Part of the Springer Series in Statistics book series (SSS, volume 0)

Abstract

The theme of this chapter is to model the relationship between a continuous response variable Y and a set of p predictors, x 1,…, x p , based on observations \(\left\{ {x_{i1},\cdots,x_{ip},Y_i } \right\}_1^N\). We assume that the underlying data structure can be described by \(Y = f(x_1,...,x_p ) + \varepsilon,\)where f is an unknown smooth function and e is the measurement error with mean zero but unknown distribution.

Keywords

Basis Function Regression Tree Multivariate Adaptive Regression Spline Continuous Response Spline Model 
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, LLC 2010

Authors and Affiliations

  1. 1.Department of Epidemiology and Public HealthYale University School of MedicineNew HavenUSA
  2. 2.Emerging Pathogens InstituteUniversity of FloridaGainesvilleUSA

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