Maximum Likelihood Estimation

Logic and Practice

by Scott R. Eliason

Series: Quantitative Applications in the Social Sciences (No. 96)

Preview
Subject categories
ISBNs
  • 9780803941076
  • 9781506315904

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.


Subject categories
ISBNs
  • 9780803941076
  • 9781506315904
Subject categories
ISBNs
  • 9780803941076
  • 9781506315904