Statistical Factor Analysis and Related Methods: Theory and Applications

Statistical Factor Analysis and Related Methods Theory and Applications In bridging the gap between the mathematical and statistical theory of factor analysis, this new work represents the first unified treatment of the theory and practice of factor analysis and latent variable models. It focuses on such areas as:
* The classical principal components model and sample-population inference
* Several extensions and modifications of principal components, including Q and three-mode analysis and principal components in the complex domain
* Maximum likelihood and weighted factor models, factor identification, factor rotation, and the estimation of factor scores
* The use of factor models in conjunction with various types of data including time series, spatial data, rank orders, and nominal variable
* Applications of factor models to the estimation of functional forms and to least squares of regression estimators

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Provides a unified treatment of both the theory and practice of factor analysis and latent variables models, for senior undergraduate and graduate students with a good background in statistics and mathematics, as well as for research workers in the empirical sciences who may wish to better acquaint themselves with the theory of latent variables. Although stress is placed on mathematical and statistical theory, this is generally reinforced by examples taken from the various areas of the natural and social sciences as well as engineering and medicine. Annotation c. Book News, Inc., Portland, OR (booknews.com)

About the author ALEXANDER BASILEVSKY is Professor of Mathematics and Statistics at the University of Winnipeg. He frequently serves as a professional consultant to both government and industry. In addition to numerous scholarly papers and government reports, Professor Basilevsky is the author of Applied Matrix Algebra in Statistical Sciences and coauthor of An Analysis of the U.S. Income Maintenance Experiments. He is a member of the Canadian Statistical Association, the American Statistical Association, and the Statistical Association of Manitoba, of which he is former president-at-large. Professor Basilevsky received his PhD in statistics/econometrics from the University of Southampton, England.