Maximum Likelihood Estimation Logic and Practice

by Scott R. Eliason

Part of the Quantitative Applications in the Social Sciences Series

Maximum Likelihood Estimation Logic and Practice Synopsis

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.

Book Information

ISBN: 9780803941076
Publication date: 29th September 1993
Author: Scott R. Eliason
Publisher: SAGE Publications Inc
Format: Paperback
Pagination: 96 pages
Categories: Social research & statistics,

About Scott R. Eliason

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

More About Scott R. Eliason

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