In this final chapter, I briefly discuss models that have been developed for data that are generally more complex than what we have thus far observed. The sections are meant to introduce you to these methods if you have not already read about them – or used them.

The following list shows the types of data situations with which you may be confronted, together with a type of model that can be used in such a situation (given in parentheses).

Types of Data and Problems Dealt with in This Chapter

1. • Data sets are small and count data highly unbalanced. (exact Poisson)

2. • Counts have been truncated or censored at the left, right (highest values), or middle areas of the distribution. (truncated and censored Poisson and NB models)

3. • The count response appears to have two or more components, each being generated by a different mechanism. (finite mixture model)

4. • One or more model predictors are ill-shaped, needing smoothing. (GAM smoothers)

5. • Counts are erratically distributed and do not appear to follow a parametric count distribution very well. (quantile count models)

6. • Data are longitudinal or clustered in nature, where observations are not independent. (panel models; e.g., GEE, GLMM)

7. • Data are nested in levels or are in a hierarchical structure. (multilevel models)

A very brief overview of the Bayesian modeling of count data will be presented in Section 9.8.