identity, g(μ) = μ
log, g(μ) = log(μ)
The platform fits a generalized linear model to the data by maximum likelihood estimation of the parameter vector. In general, there is no closed-form solution for the maximum likelihood estimates of the parameters. Therefore, the platform estimates the parameters of the model numerically through an iterative fitting process using a technique pioneered by Nelder and Wedderburn (1972). The overdispersion parameter φ is estimated by dividing the Pearson goodness-of-fit statistic by its degrees of freedom. Covariances, standard errors, and confidence limits are computed for the estimated parameters based on the asymptotic normality of maximum likelihood estimators.
g(μ) = μ
g(μ) = Φ-1(μ), where Φ is the standard normal cumulative distribution function
g(μ) = log(μ)
g(μ) =
Table 11.3 lists the variance functions associated with the available distributions for the response variable.
V(μ) = 1
V(μ) = μ(1 – μ)
V(μ) = μ
V(μ) = μ2

Help created on 7/12/2018