Generalized linear models
Article first published online: 8 JUN 2011
Copyright © 2011 John Wiley & Sons, Inc.
Wiley Interdisciplinary Reviews: Computational Statistics
Volume 3, Issue 5, pages 407–413, September/October 2011
How to Cite
Neuhaus, J. and McCulloch, C. (2011), Generalized linear models. WIREs Comp Stat, 3: 407–413. doi: 10.1002/wics.175
- Issue published online: 2 AUG 2011
- Article first published online: 8 JUN 2011
- predictor variables;
The class of generalized linear models (GLMs) extends the classical linear model for continuous, normal responses to describe the relationship between one or more predictor variables x1,…,xp and a wide variety of nonnormally distributed responses Y including binary, count, and positive-valued variates. GLMs expand the class of response densities from the normal to an exponential family that contains the normal, Poisson, binomial, and other popular distributions as special cases. The models produce estimated expected values that conform to response constraints and allow nonlinear relationships between predictors and expected values. It is straightforward to construct the likelihood for a set of data so that maximum likelihood and related likelihood-based methods are popular techniques for parameter estimation and inference. A key point with GLMs is that many of the considerations in model construction are the same as for standard linear regression models as the models have many common features. WIREs Comp Stat 2011 3 407–413 DOI: 10.1002/wics.175
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