Abstract
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 X T β 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.
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© 1995 Springer-Verlag New York, Inc.
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Hilbe, J., Turlach, B.A. (1995). Generalized Linear Models. In: XploRe: An Interactive Statistical Computing Environment. Statistics and Computing. Springer, New York, NY. https://doi.org/10.1007/978-1-4612-4214-7_10
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DOI: https://doi.org/10.1007/978-1-4612-4214-7_10
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