Maximum Likelihood Estimation. . . provides a useful introduction. . . it is clear and easy to follow with applications and graphs. . . . I consider this a very useful book. . . . well-written, with a wealth of explanation. . ." --Dougal Hutchison in Educational Research Eliason reveals to the reader the underlying logic and practice of maximum likelihood (ML) estimation 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 (such as the normal error regression model) 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 maximum likelihood solutions, the general form of the covariance matrix for ML estimates, the sampling distribution of ML estimators; the use of ML in the normal as well as other distributions, and some useful illustrations of likelihoods.
With the passing of Clifford Collier Clogg at the age of 45 on May 7th 1995, the world lost a talented sociologist, demographer, and statistician all at once. In addition to being a considerable talent in each of these three disciplines, and perhaps more importantly, Cliff was the type of person who brought to gether diverse elements and scholars from all three. Cliff was also a consum mate mentor, nurturing ideas and students and always striving to bring out the best in both. Perhaps nothing illustrates the stature, impact, and respect others held for Cliff more than the fact that never before-and never since has an individual been honored at the time of his death with ceremonies from the national associations of all three of these disciplines. The purpose of this book is to introduce to a broad constituency of social scientists and their students some of the basic ideas in the study of the labor force that Cliff and his colleagues had grappled with. 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. Our goal is to highlight Cliff's substantive contributions to sociology and demography by telling the cumulative story of his research and adding updated analysis that advances the story beyond the early 1980s to the mid-1990s.
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