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Abstract Details
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Activity Number:
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574
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Type:
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Contributed
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Date/Time:
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Wednesday, August 3, 2011 : 2:00 PM to 3:50 PM
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Sponsor:
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Section on Statistical Computing
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| Abstract - #302745 |
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Title:
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Geometry of Generalized Linear Models
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Author(s):
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George R. Terrell*+
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Companies:
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Virginia Polytechnic Institute and State University
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Address:
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Statistics Department, Blacksburg, VA, 24061,
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Keywords:
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Loglinear models ;
logistic regression ;
convex regression ;
duality
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Abstract:
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It has long been found useful to think of classical linear regression geometrically, as involving predictions and errors that lie in certain linear subspaces of observation space. Generalized linear models, such as logistic regression, by contrast, are usually formulated in terms of their likelihood. We will here show that a rich class of models, formulated in terms of vector geometry in observation space, includes the generalized linear models with canonical link function. Their geometry is formally dual to the classical case. The new characterization simplifies computation in a number of cases.
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