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.
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© 1994 Springer Science+Business Media New York
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Kleinbaum, D.G. (1994). Maximum Likelihood Techniques: An Overview. In: Logistic Regression. Statistics in the Health sciences. Springer, New York, NY. https://doi.org/10.1007/978-1-4757-4108-7_4
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DOI: https://doi.org/10.1007/978-1-4757-4108-7_4
Publisher Name: Springer, New York, NY
Print ISBN: 978-1-4757-4110-0
Online ISBN: 978-1-4757-4108-7
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