Maximum Likelihood Estimation Logic and Practice
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Description: 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.
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All the information you need in one place! Each Study Brief is a summary of one specific subject; facts, figures, and explanations to help you learn faster.
List price: $19.00
Copyright year: 1993
Publisher: SAGE Publications, Incorporated
Publication date: 8/9/1993
Size: 5.50" wide x 8.50" long x 0.25" tall
|The Logic of Maximum Likelihood|
|A General Modeling Framework Using Maximum Likelihood Methods|
|An Introduction to Basic Estimation Techniques|
|Further Empirical Examples|