Generalized Linear Model

In statistics, a generalized linear model (GLM) is a model relating the expected value E(y) of a dependent variable y to one or more independent variables x1, ..., xn, with the relation stated as follows.
\mu = \mbox{E}(y) \,
g(\mu) = a_1 x_1 + \cdots + a_n x_n
where g is an invertible function, called the link function. Each specific choice of the link function and the distribution for the dependent variable yields a different generalized linear model. Generalized linear models include, as special cases, ordinary linear regression, logistic regression, Poisson regression, and several other interesting models.

References

  • P. McCullagh and J.A. Nelder. Generalized Linear Models. London: Chapman and Hall, 1989.

External links

 

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