Bøger af Scott R. Eliason

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.

At the time of Cliff's death, he was perhaps better known for his methodological contributions to sociology and demography than he was for his substantive contributions to the study of social stratification and the labor force.