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	Unavailable
ISBN 13	9780803941076
ISBN 10	0803941072
Title	Maximum Likelihood Estimation
Author	Scott R Eliason
Series	Quantitative Applications In The Social Sciences
Condition	Unavailable
Binding Type	Paperback
Publisher	SAGE Publications Inc
Year published	1993-09-29
Number of pages	96
Cover note	Book picture is for illustrative purposes only, actual binding, cover or edition may vary.
Note	Unavailable