Using regression mixture models with non-normal data: Examining an ordered polytomous approach

Melissa R.W. George, N. Yang, M. Lee Van Horn, Jessalyn Smith, Thomas Jaki, Daniel J. Feaster, Katherine Masyn, George Howe

Research output: Contribution to journalArticlepeer-review

12 Scopus citations


Mild to moderate skew in errors can substantially impact regression mixture model results; one approach for overcoming this includes transforming the outcome into an ordered categorical variable and using a polytomous regression mixture model. This is effective for retaining differential effects in the population; however, bias in parameter estimates and model fit warrant further examination of this approach at higher levels of skew. The current study used Monte Carlo simulations; 3000 observations were drawn from each of two subpopulations differing in the effect of X on Y. Five hundred simulations were performed in each of the 10 scenarios varying in levels of skew in one or both classes. Model comparison criteria supported the accurate two-class model, preserving the differential effects, while parameter estimates were notably biased. The appropriate number of effects can be captured with this approach but we suggest caution when interpreting the magnitude of the effects.

Original languageEnglish (US)
Pages (from-to)759-772
Number of pages14
JournalJournal of Statistical Computation and Simulation
Issue number4
StatePublished - Apr 2013


  • differential effects
  • non-normal errors
  • regression mixture models

ASJC Scopus subject areas

  • Applied Mathematics
  • Statistics and Probability
  • Modeling and Simulation
  • Statistics, Probability and Uncertainty


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