The University of Arizona
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Bias reduction in multi-class logistic regression and the problem of separation.

Multi-lingual Optical Character Recognition Seminar

Bias reduction in multi-class logistic regression and the problem of separation.
Series: Multi-lingual Optical Character Recognition Seminar
Location: ENR2 S375
Presenter: Raymundo Navarette, University of Arizona, Post Doc.

 We will go over the basics of Firth's method for bias reduction in maximum likelihood estimates and apply them to multi-class logistic regression. We will discuss how this and other penalization approaches remove the problem of separation, which occurs when sample sizes are small or when all classes can be separated with linear classifiers, and leads to non-existent (infinite) optimal parameters.