EXPONENTIAL DISPERSION MODELS AND THE GAUSS‐NEWTON ALGORITHM
作者:
Gordon K. Smyth1,
期刊:
Australian Journal of Statistics
(WILEY Available online 1991)
卷期:
Volume 33,
issue 1
页码: 57-64
ISSN:0004-9581
年代: 1991
DOI:10.1111/j.1467-842X.1991.tb00412.x
出版商: Blackwell Publishing Ltd
关键词: Key woids: Generalized linear models;scoring algorithm;multinomial distribution;quasi‐likelihood
数据来源: WILEY
摘要:
SummaryIt is 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 preserved. The multinomial distribution is used as an exampl
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