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.
Posted at April 03, 2012 09:15 | permanent link