### Factor Analysis (Advanced Data Analysis from an Elementary Point of View)

Adding noise to PCA to get a statistical model. The factor model, or linear
regression with unobserved independent variables. Assumptions of the factor
model. Implications of the model: observable variables are correlated only
through shared factors; "tetrad equations" for one factor models, more general
correlation patterns for multiple factors. Our first look at latent variables
and conditional independence. Geometrically, the factor model says the data
cluster on some low-dimensional plane, plus noise moving them off the plane.
Estimation by heroic linear algebra; estimation by maximum likelihood. The
rotation problem, and why it is unwise to reify factors. Other models which
produce the same correlation patterns as factor models.

*Reading*: Notes, chapter 18;
`factors.R` and
`sleep.txt`

Advanced Data Analysis from an Elementary Point of View

Posted at March 28, 2013 10:30 | permanent link