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Conditional Stereotype Logistic Regression: A New Estimation Command

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  • Rob Woodruff

    () (Battelle Memorial Institute: Public Health and Statistics Group)

Abstract

The stereotype logistic regression model for a categorical dependent variable is often described as a compromise between the multinomial and proportional-odds logistic models, and has many attractive features. Among these are the ability to test the adequacy of the model fit compared to the unconstrained multinomial model, to test the distinguishability of the outcome categories, and even to test the 'ordinality' assumption itself. What brought me to write the new command however, was the desire to take advantage of these capabilities while working on a matched, case-control study. Like the multinomial logistic model (and unlike the proportional-odds model), the stereotype model yields valid inference under outcome dependent sampling designs, and can be much more parsimonious. The working title of my command is -cstereo-, and it is implemented using the d2-method of Stata's -ml- command. In terms of existing Stata capabilities: -clogit- is to -logit- as -cstereo- is to -slogit-. In this talk I will demonstrate the command's features using a simulated matched, case-control dataset.

Suggested Citation

  • Rob Woodruff, 2013. "Conditional Stereotype Logistic Regression: A New Estimation Command," 2013 Stata Conference 11, Stata Users Group.
  • Handle: RePEc:boc:norl13:11
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