Description: 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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