Statistical Factor Analysis and Related Methods: Theory and Applications / Edition 1 available in Hardcover
- Pub. Date:
Statistical Factor Analysis and Related Methods Theory andApplications In bridging the gap between the mathematical andstatistical theory of factor analysis, this new work represents thefirst unified treatment of the theory and practice of factoranalysis and latent variable models. It focuses on such areasas:
* The classical principal components model and sample-populationinference
* Several extensions and modifications of principal components,including Q and three-mode analysis and principal components in thecomplex domain
* Maximum likelihood and weighted factor models, factoridentification, factor rotation, and the estimation of factorscores
* The use of factor models in conjunction with various types ofdata including time series, spatial data, rank orders, and nominalvariable
* Applications of factor models to the estimation of functionalforms and to least squares of regression estimators
About the Author
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.
Table of Contents
Matrixes, Vector Spaces.
The Ordinary Principal Components Model.
Statistical Testing of the Ordinary Principal ComponentsModel.
Extensions of the Ordinary Principal Components Model.
Factor Analysis of Correlated Observations.
Ordinal and Nominal Random Data.
Other Models for Discrete Data.
Factor Analysis and Least Squares Regression.