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

Maximum Likelihood Estimation

Logic and Practice

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Published: 9th August 1993
Format: ePUB
$24.95

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.

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
Table of Contents provided by Ingram. All Rights Reserved.

ISBN: 9780803941076
ISBN-10: 0803941072
Series: Quantitative Applications in the Social Sciences
Audience: Professional
Format: Paperback
Language: English
Number Of Pages: 96
Published: 9th August 1993
Country of Publication: US
Dimensions (cm): 21.5 x 13.9  x 0.61
Weight (kg): 0.12