Least likely observations in regression models for categorical outcomes
Jeremy Freese
University of Wisconsin–Madison
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Abstract. This article presents a method and program for identifying poorly fitting
observations for maximum-likelihood regression models for categorical
dependent variables. After estimating a model, the program
leastlikely will list the observations that have the lowest predicted
probabilities of observing the value of the outcome category that was
actually observed. For example, when run after estimating a binary logistic
regression model, leastlikely will list the observations with a
positive outcome that had the lowest predicted probabilities of a positive
outcome and the observations with a negative outcome that had the lowest
predicted probabilities of a negative outcome. These can be considered the
observations in which the outcome is most surprising given the values of the
independent variables and the parameter estimates and, like observations
with large residuals in ordinary least squares regression, may warrant
individual inspection. Use of the program is illustrated with examples
using binary and ordered logistic regression.
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Jeremy Freese
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outliers, predicted probabilities, categorical dependent variables, logistic regression
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