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Thesis

Linear programming algorithms for detecting separated data in binary logistic regression models

Abstract:

This thesis is a study of the detection of separation among the sample points in binary logistic regression models. We propose a new algorithm for detecting separation and demonstrate empirically that it can be computed fast enough to be used routinely as part of the fitting process for logistic regression models. The parameter estimates of a binary logistic regression model fit using the method of maximum likelihood sometimes do not converge to finite values. This phenomenon (also known as ...

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Institution:
University of Oxford
Oxford college:
Worcester College
Department:
Mathematical,Physical & Life Sciences Division - Statistics
Role:
Author

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Role:
Supervisor
Publication date:
2007
Type of award:
DPhil
Level of award:
Doctoral
URN:
uuid:8f9ee0d0-d78e-4101-9ab4-f9cbceed2a2a
Local pid:
ora:2848

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