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Informationen zum Autor 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. Klappentext 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 Zusammenfassung The purpose of this work is to provide a unified treatment of both the theory and practice of factor analysis and latent variables models. Inhaltsverzeichnis Preliminaries. Matrixes, Vector Spaces. The Ordinary Principal Components Model. Statistical Testing of the Ordinary Principal ComponentsModel. Extensions of the Ordinary Principal Components Model. Factor Analysis. Factor Analysis of Correlated Observations. Ordinal and Nominal Random Data. Other Models for Discrete Data. Factor Analysis and Least Squares Regression. Exercises. References. Index....
