A Solution to separation in binary response models

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Abstract

A common problem in models for dichotomous dependent variables is "separation," which occurs when one or more of a model's covariates perfectly predict some binary outcome. Separation raises a particularly difficult set of issues, often forcing researchers to choose between omitting clearly important covariates and undertaking post-hoc data or estimation corrections. In this article I present a method for solving the separation problem, based on a penalized likelihood correction to the standard binomial GLM score function. I then apply this method to data from an important study on the postwar fate of leaders.

Original languageEnglish (US)
Pages (from-to)157-170
Number of pages14
JournalPolitical Analysis
Volume13
Issue number2
DOIs
StatePublished - 2005

All Science Journal Classification (ASJC) codes

  • Sociology and Political Science
  • Political Science and International Relations

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