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Description - Maximum Likelihood Estimation by Scott R. Eliason

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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Book Details

ISBN: 9780803941076
ISBN-10: 0803941072
Format: Paperback
(215mm x 139mm x 6mm)
Pages: 96
Imprint: SAGE Publications Inc
Publisher: SAGE Publications Inc
Publish Date: 1-Sep-1993
Country of Publication: United States

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Author Biography - Scott R. Eliason

RESEARCH AND TEACHING INTERESTS Quantitative Methodology and Statistics; Sociology of Work, Occupations, and Labor Markets; Economic Sociology; Stratification; Life Course