Maximum Likelihood Estimation: Logic and Practice: Quantitative Applications in the Social Sciences, cartea 96
Autor Scott R. Eliasonen Limba Engleză Paperback – 28 sep 1993
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Specificații
ISBN-13: 9780803941076
ISBN-10: 0803941072
Pagini: 96
Dimensiuni: 140 x 216 x 6 mm
Greutate: 0.12 kg
Ediția:Will Be Reissue.
Editura: SAGE Publications
Colecția Sage Publications, Inc
Seria Quantitative Applications in the Social Sciences
Locul publicării:Thousand Oaks, United States
ISBN-10: 0803941072
Pagini: 96
Dimensiuni: 140 x 216 x 6 mm
Greutate: 0.12 kg
Ediția:Will Be Reissue.
Editura: SAGE Publications
Colecția Sage Publications, Inc
Seria Quantitative Applications in the Social Sciences
Locul publicării:Thousand Oaks, United States
Cuprins
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
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
Notă biografică
RESEARCH AND TEACHING INTERESTS
Quantitative Methodology and Statistics; Sociology of Work, Occupations, and Labor Markets;
Economic Sociology; Stratification; Life Course
Quantitative Methodology and Statistics; Sociology of Work, Occupations, and Labor Markets;
Economic Sociology; Stratification; Life Course
Descriere
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.