With the release of Stata 7, the capabilities of glm were greatly enhanced. Among the improvements was the ability for users to program their own custom link and variance functions. Whereas previously glm was used primarily as a platform on which to compare the results of standard regression models (such as the logistic, probit, and Poisson), it may now be utilised to perform generalized maximum pseudo-likelihood estimation in any framework. Thus far, this has been an ability that for the most part has not been exploited. The method by which user-defined links and variance functions may be incorporated is quite straightforward, as demonstrated in the companion text to glm by Hardin and Hilbe (Stata Press, 2001). In this talk, I present a few examples of case studies from the literature where the science dictated the fitting of a generalized linear model with special (non-standard) link and/or variance function. I demonstrate how these models (which were typically fit using SAS's GENMOD procedure) may be fit using Stata.
