ABSTRACT
Likelihood Inference and the
Generalized Linear Model
2.1 Motivation
This chapter is all about mathematical statistics prerequisites and prelim-
inaries. The core objective here is to provide a description of likelihood
inference and estimation as a interim goal on the path to fully understand-
ing Bayesian models. Since Bayesian inference is a combination of prior
information and sample information from the likelihood function, under-
standing likelihood is important to our purposes. The second component of
this chapter, a description of generalized linear models, is also important
in that it provides a unied means of conceptualizing parametric speci-
cations. This framework is one that is sorely needed in the social and
behavioral sciences as most students learn this material as a disparate set
of unconnected techniques.
