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Maximum Likelihood Estimation Logic and Practice by Scott R. Eliason
  

Maximum Likelihood Estimation Logic and Practice

Part of the Quantitative Applications in the Social Sciences Series

Synopsis

Maximum Likelihood Estimation Logic and Practice 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.

About the Author

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

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Book Info

Publication date

9th August 1993

Author

Scott R. Eliason

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Publisher

SAGE Publications Inc

Format

Paperback
96 pages

Categories

Social research & statistics

ISBN

9780803941076

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