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Maximum Likelihood Estimation
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Maximum Likelihood Estimation
Logic and Practice



September 1993 | 96 pages | SAGE Publications, Inc
In this volume the underlying logic and practice of maximum likelihood (ML) estimation is made clear by providing a general modelling framework that utilizes the tools of ML methods. This framework offers readers a flexible modelling strategy since it accommodates cases from the simplest linear models to the most complex nonlinear models that link a system of endogenous and exogenous variables with non-normal distributions. Using examples to illustrate the techniques of finding ML estimators and estimates, Eliason discusses: what properties are desirable in an estimator; basic techniques for finding ML solutions; the general form of the covariance matrix for ML estimates; the sampling distribution of ML estimators; the application of ML in the normal distribution as well as in other useful distributions; and some helpful illustrations of likelihoods.
 
Introduction
The Logic of Maximum Likelihood

 
 
A General Modeling Framework Using Maximum Likelihood Methods
 
An Introduction to Basic Estimation Techniques
 
Further Empirical Examples
 
Additional Likelihoods
 
Conclusions

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ISBN: 9780803941076
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