Generalized Additive Models

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

Abstract

The multiple linear regression model discussed in Chapter 8 and the generalized linear model covered in Chapters 9 and 10 accommodate nonlinear relationships between the response variable (or the link function of its mean) and one or more of the explanatory variables by using polynomial terms or parametric transformations. (The predictor remains linear in the parameters, of course; nonlinear models are nonlinear in their parameters and are the subject of Chapter 14.) In this chapter, however, we consider some more flexible models in which the relationship between the response variable and one or more of the explanatory variables is modeled by using some type of scatterplot smoother (these were introduced informally in earlier chapters—see, for example, Chapter 4); their use here allows the data to suggest the form of the relationship involved, and indirectly to suggest whether the data might be better modeled by a linear or generalized linear model which included polynomial terms of a particular degree for some, or all, of the explanatory variables.

Keywords

Explanatory Variable Cystic Fibrosis Erythrocyte Sedimentation Rate Additive Model Multiple Linear Regression 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.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

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

Personalised recommendations