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Erschienen in:
1995 | OriginalPaper | Buchkapitel
Generalized Linear Models
verfasst von : Joseph Hilbe, Berwin A. Turlach
Erschienen in: XploRe: An Interactive Statistical Computing Environment
Verlag: Springer New York
Enthalten in: Professional Book Archive
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In this chapter we shall discuss a class of statistical models that generalize the well-understood normal linear model. A normal or Gaussian model assumes that the response Y is equal to the sum of a linear combination XTβ of the d-dimensional predictor X and a Gaussian distributed error term. It is well known that the least-squares estimator $$\hat \beta $$ of β performs well under these assumptions. Moreover, extensive diagnostic tools have been developed for models of this type.