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

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

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