| Summary: | The concept of collinearity for generalized linear models is introduced and compared to that for standard linear models. Two approaches for detecting collinearity are presented and shown to lead to the same diagnostic procedure. These are analysed for the Poisson, gamma, inverse Gaussian, pth order, binomial proportion and negative binomial models. A bound is derived for the degree of collinearity in a generalized linear model in terms of that of the standard linear model. Estimation methods based on ridge, prior likelihood and principal components are proposed, and briefly illustrated with a Monte Carlo simulation of a gamma model. === Business, Sauder School of === Graduate
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