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Generalized Linear Models, Second Edition


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Authors:
P. McCullagh and J. A. Nelder
Publisher: Chapman & Hall/CRC
Copyright: 1989
ISBN-13: 978-0-412-31760-6
Pages: 511; hardcover
Price: $94.75
Authors:
P. McCullagh and J. A. Nelder
Publisher: Chapman & Hall/CRC
Copyright: 1989
ISBN-13:
Pages: 511; eBook
Price: $0.00

Comment from the Stata technical group

This book covers the methodology of generalized linear models, which has evolved dramatically over the last 20 years as a way to generalize the methods of classical linear regression to more complex situations, including analysis-of-variance models, logit and probit models, log-linear models, models with multinomial responses for counts, and models for survival data. Although the original least-squares estimation for linear regression is based on Gaussian errors, the most important properties of least-squares estimates depend only on the assumed mean-to-variance relationship and on the statistical independence of the observations. This fact is exploited in developing the more general algorithm of iteratively reweighted least-squares to handle the more complex models.

Considered by many to be the most thorough treatment on the topic, this text is organized to be accessible to the practicing research scientist with only the most basic knowledge of statistical theory.

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