- 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
Matrixes, Vector Spaces.
The Ordinary Principal Components Model.
Statistical Testing of the Ordinary Principal Components Model.
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.