Exponential Dispersion Models and the Gauss-Newton Algorithm
نویسنده
چکیده
It is well known that the Fisher scoring iteration for generalized linear models has the same form as the Gauss-Newton algorithm for normal regression. This note shows that exponential dispersion models are the most general families to preserve this form for the scoring iteration. Therefore exponential dispersion models are the most general extension of generalized linear models for which the analogy with normal regression is maintained. The multinomial distribution is used as an example.
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تاریخ انتشار 1991