Factor Models and Principal Components
High-dimensional data can be challenging to analyze. They are difficult to visualize, need extensive computer resources, and often require special statistical methodology. Fortunately, in many practical applications, high-dimensional data have most of their variation in a lower-dimensional space that can be found using dimension reduction techniques. There are many methods designed for dimension reduction, and in this chapter we will study two closely related techniques, factor analysis and principal components analysis, often called PCA.