Model evaluation, or the more stronger term, model validation can be defined as the objective assessment of the predictive performance of a model. As the science of model evaluation is evolving, no formal guidance can be given regarding the model evaluation method to be used. There is no "right" or "wrong" model in that a model can be valid for one purpose and not for another. Consequently, any model evaluation procedure should take into account the intended use of the model.
An examination of over thirty published papers where a variety of model evaluation procedures were performed reveals that the most common method is to predict a relevant variable or parameter, using a separate external "validation" data set. Other reported techniques included cross validation and use of the "posterior predictive check".
The presentation to be given will give an overview of the various possible diagnostic tools and model evaluation procedures via several examples, and will highlight their respective advantages and disadvantages, in light of the intended use of the model.