Regression Models for Categorical and Limited Dependent Variables (Advanced Quantitative Techniques in the Social Sciences)
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.
Country | USA |
Brand | Sage Publications |
Manufacturer | SAGE Publications, Inc |
Binding | Paperback |
ItemPartNumber | 9780803941076 |
Model | 9780803941076 |
ReleaseDate | 1993-08-09 |
UnitCount | 1 |
EANs | 9780803941076 |