Maximum Likelihood Estimation
Logic and Practice
Nonfiction, Reference & Language, Reference, Research, Social & Cultural Studies, Social Science
| Author: | Scott R. Eliason | ISBN: | 9781506315904 |
| Publisher: | SAGE Publications | Publication: | August 9, 1993 |
| Imprint: | SAGE Publications, Inc | Language: | English |
| Author: | Scott R. Eliason |
| ISBN: | 9781506315904 |
| Publisher: | SAGE Publications |
| Publication: | August 9, 1993 |
| Imprint: | SAGE Publications, Inc |
| Language: | English |
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.
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.
