Maximum Likelihood Techniques: An Overview

  • David G. Kleinbaum
Part of the Statistics in the Health sciences book series (SBH)

Abstract

In this chapter, we describe the general maximum likelihood (ML) procedure, including a discussion of likelihood functions and how they are maximized. We also distinguish between two alternative ML methods, called 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.

Keywords

Logistic Model Likelihood Function Statistical Inference Nuisance Parameter Discriminant Function Analysis 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer Science+Business Media New York 1994

Authors and Affiliations

  • David G. Kleinbaum
    • 1
  1. 1.Department of EpidemiologyEmory UniversityAtlantaUSA

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