Maximum Likelihood Estimation by Scott R Eliason

Maximum Likelihood Estimation by Scott R Eliason

"Maximum Likelihood Estimation. . . provides a useful introduction. . . it is clear and easy to follow with applications and graphs. . . . I consider this a very useful book. . . . well-written, with a wealth of explanation. . ." --Dougal Hutchison in Educational Research Eliason reveals to the reader the underlying logic and practice of maximum likelihood (ML) estimation 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 (such as the normal error regression model) 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 maximum likelihood solutions, the general form of the covariance matrix for ML estimates, the sampling distribution of ML estimators; the use of ML in the normal as well as other distributions, and some useful illustrations of likelihoods.

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

SKU	Non disponible
ISBN 13	9780803941076
ISBN 10	0803941072
Titre	Maximum Likelihood Estimation
Auteur	Scott R Eliason
Série	Quantitative Applications In The Social Sciences
État	Non disponible
Type de reliure	Paperback
Éditeur	SAGE Publications Inc
Année de publication	1993-09-29
Nombre de pages	96
Note de couverture	La photo du livre est présentée à titre d'illustration uniquement. La reliure, la couverture ou l'édition réelle peuvent varier.
Note	Non disponible