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Generalized linear models : a unified approach

Author: Jeff Gill
Publisher: Thousand Oaks, Calif. : Sage Publications, ©2001.
Series: Sage university papers series., Quantitative applications in the social sciences ;, no. 134.
Edition/Format:   Book : EnglishView all editions and formats
Database:WorldCat
Summary:
"The author explains the theoretical underpinnings of generalized linear models so that researchers can decide how to select the best way to adapt their data for this type of analysis. Examples are provided to illustrate the application of GLM to actual data and the author includes his Web address where additional resources can be found."--pub. desc.
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Details

Material Type: Internet resource
Document Type: Book, Internet Resource
All Authors / Contributors: Jeff Gill
ISBN: 0761920552 9780761920557
OCLC Number: 44578750
Description: viii, 101 p. : ill. ; 22 cm.
Contents: Model Specification --
Probability Distributions --
The Linear Model --
Linear Algebra and Calculus --
Software --
Looking Forward --
The Exponential Family --
Justification --
Derivation --
Canonical Form --
Multiparameter Models --
Likelihood Theory and the Moments --
Maximum Likelihood Estimation --
Calculating the Mean of the Exponential Family --
Calculating the Variance of the Exponential Family --
The Variance Function --
Linear Structure and the Link Function --
The Generalization --
Distributions --
Estimation Procedures --
Estimation Techniques --
Newton-Raphson and Root Finding --
Weighted Least Squares --
Iterative Weighted Least Squares --
Residuals and Model Fit --
Defining Residuals --
Anscombe Residuals --
The Deviance Function and Deviance Residuals --
Measuring and Comparing Goodness of Fit --
Asymptotic Properties --
Motivation.
Series Title: Sage university papers series., Quantitative applications in the social sciences ;, no. 134.
Responsibility: Jeff Gill.
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Abstract:

The author explains the theoretical underpinnings of generalized linear models so that researchers can decide how to select the best way to adapt their data for this type of analysis. Examples are  Read more...

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