In this volume the underlying logic and practice of maximum likelihood (ML) estimation is made clear by providing a general modelling framework that utilizes the tools of ML methods. This framework offers readers a flexible modelling 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.
|Publication date:||29th September 1993|
|Author:||Scott R. Eliason|
|Publisher:||SAGE Publications Inc|
|Categories:||Social research & statistics,|
RESEARCH AND TEACHING INTERESTS Quantitative Methodology and Statistics; Sociology of Work, Occupations, and Labor Markets; Economic Sociology; Stratification; Life CourseMore About Scott R. Eliason