You are here

Maximum Likelihood Estimation
Share

Maximum Likelihood Estimation
Logic and Practice



August 1993 | 96 pages | SAGE Publications, Inc

"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.

Learn more about "The Little Green Book" - QASS Series! Click Here


 
Introduction
The Logic of Maximum Likelihood

 
 
A General Modeling Framework Using Maximum Likelihood Methods
 
An Introduction to Basic Estimation Techniques
 
Further Empirical Examples
 
Additional Likelihoods
 
Conclusions

Preview this book

For instructors

To inquire about the availability of this title for review (print and/or digital), please contact your local sales representative or call (800) 818-7243.

Select a Purchasing Option

ISBN: 9780803941076
$22.00

This title is also available on SAGE Knowledge, the ultimate social sciences online library. If your library doesn’t have access, ask your librarian to start a trial.