Stochastic Processes and Generalized Linear Models

Abstract

Most of the models presented up until now illustrate how the commonly used and classical approaches to stochastic processes can be handled in GLIM. Many demonstrate the versatility of writing macros in the GLIM programming language. This flexibility can obviously be extended to many other models, either in the family of generalized linear models, or, at least, closely related to it. For example, the models for time series in the time domain, presented in Chapter 6, all use a normal distribution. They can very simply be modified to accommodate any other member of the exponential family. Similar modifications could be applied to certain models in other chapters. In this chapter, we shall consider a heterogeneous collection of models for stochastic processes which bring together some of the ideas of the previous chapters.