Maximum Likelihood Estimation: Logic and Practice

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.

1115599061
Maximum Likelihood Estimation: Logic and Practice

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.

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Maximum Likelihood Estimation: Logic and Practice

Maximum Likelihood Estimation: Logic and Practice

by Scott R. Eliason
Maximum Likelihood Estimation: Logic and Practice

Maximum Likelihood Estimation: Logic and Practice

by Scott R. Eliason

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Overview

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.


Product Details

ISBN-13: 9781506315904
Publisher: SAGE Publications
Publication date: 08/09/1993
Series: Quantitative Applications in the Social Sciences , #96
Sold by: Barnes & Noble
Format: eBook
Pages: 96
File size: 3 MB

About the Author

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

Table of Contents

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
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