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3 - Maximum Likelihood for Binary Outcomes

from Part I - Concepts, Theory, and Implementation

Published online by Cambridge University Press:  15 November 2018

Michael D. Ward  and
John S. Ahlquist
Michael D. Ward
Affiliation:
Duke University, North Carolina
John S. Ahlquist
Affiliation:
University of California, San Diego
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Summary

Builds likelihood-based models for binary data and describes how we can evaluate the model and use sampling tools to generate meaningful interpretations of model quantities

Keywords

binary datalogitprobitbinomiallatent variableROCseparation plotinterpretation
Type
Chapter
Information
Maximum Likelihood for Social Science
Strategies for Analysis
 , pp. 43 - 78
Publisher: Cambridge University Press
Print publication year: 2018

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