Abstract:
The purpose of this paper is to develop a data augmentation technique for statistical
inference concerning stochastic cusp catastrophe model subject to missing data and partially observed
observations. We propose a Bayesian inference solution that naturally treats missing observations as
parameters and we validate this novel approach by conducting a series of Monte Carlo simulation
studies assuming the cusp catastrophe model as the underlying model. We demonstrate that this
Bayesian data augmentation technique can recover and estimate the underlying parameters from the
stochastic cusp catastrophe model.
