Maximum Likelihood Estimation: Logic and Practice

by Scott R. Eliason

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.

96 pages, Paperback

First published January 1, 1993

Reviews

[Steven Peterson · Rating 3 out of 5]

I focused on ordinary least squares in terms of multivariate statistics when in graduate school. We did not discuss very much alternative perspectives. I was a multiple regression afficianado. But there is another approach, maximum likelihood estimation (MLE). This book does a nice job of presenting a lucid explanation of MLE. Later in my academic career, I did come to appreciate some of the techniques of this in practice.