Introduction In this chapter, we describe the general maximum like-
lihood (ML) procedure, including a discussion of like-
lihood functions and how they are maximized. We also
distinguish between two alternative ML methods, the
unconditional and the conditional approaches, and we
give guidelines regarding how the applied user can
choose between these methods. Finally, we provide a
brief overview of how to make statistical inferences using
ML estimates.
Abbreviated
Outline
The outline below gives the user a preview of the material
to be covered by the presentation. Together with the objec-
tives, this outline offers the user an overview of the content
of this module. A detailed outline for review purposes
follows the presentation.I. Overview (page 106)
II. Background about maximum likelihood
procedure (pages 106–107)
III. Unconditional vs. conditional methods (pages
107–111)
IV. The likelihood function and its use in the ML
procedure (pages 111–117)
V. Overview on statistical inferences for logistic
regression (pages 117–121)104 4. Maximum Likelihood Techniques: An Overview