Maximum Likelihood Estimation

1st Edition

Logic and Practice

Scott R. Eliason
eISBN-13: 9781506315904

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In this volume the underlying logic and practice of maximum likelihood (ML) estimation is made clear by providing a general modeling framework that utilizes the tools of ML methods. This framework offers readers a flexible modeling 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.

Sold By SAGE Publications
ISBNs 9780803941076, 9780803941076, 1506315909, 9781506315904
Language English
Number of Pages 96
Edition 1st