The Ability for Posterior Predictive Checking to Identify Model Misspecification in Bayesian Growth Mixture Modeling

• In Event: Roundtable Session 60
  In Roundtable Session: A Mixture of Papers on Heterogeneity

Abstract

Proper model specification is an issue for researchers, regardless of the estimation framework being utilized. There is a procedure in the Bayesian framework called posterior predictive checking, that is designed theoretically to detect model mis-specification for observed data. However, the performance of the posterior predictive check procedure has thus far not been directly examined under different conditions of mixture model mis-specification. The current paper addresses this task and aims at providing additional insight into whether or not posterior predictive checks can detect model mis-specification within the context of Bayesian growth mixture modeling. Results indicate that this procedure can only identify mixture model mis-specification under very extreme cases of mis-specification.

Author

• Sarah Depaoli, University of California - Merced