Bayesian analysis of an item response model with an AEP-based link function.

We consider a robust Bayesian approach to the analysis of item response models, using the inverse of an asymmetric exponential power cumulative distribution function as a link function. This provides greater flexibility with respect to classic link functions such as the probit and the logit. We conduct a simulation study to evaluate the performance of our model. In order to draw samples from the posterior distribution of the parameters, we implement a Markov chain Monte Carlo scheme by means of the JAGS software. We also implement a posterior predictive model-checking method to assess the fit and relative performance of the various submodels. Finally, we provide a real-data example to illustrate the modeling approach proposed. [ABSTRACT FROM AUTHOR]