Maximum Likelihood Estimation: Logic and Practice, Issue 96

Front Cover
SAGE, 1993 - Mathematics - 87 pages

In this volume the underlying logic and practice of maximum likelihood (ML) estimation is made clear 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 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.

 

What people are saying - Write a review

We haven't found any reviews in the usual places.

Contents

A General Modeling Framework Using Maximum
21
An Introduction to Basic Estimation Techniques
39
Further Empirical Examples
46
Additional Likelihoods
62
Conclusions
68
Notes
83
Copyright

Other editions - View all

Common terms and phrases

About the author (1993)

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

Bibliographic information