Predictor-dependent shrinkage for linear regression via partial factor modeling

Abstract

In prediction problems with more predictors than observations, it can sometimes be helpful to use a joint probability model, $\pi (Y,X)$, rather than a purely conditional model, $\pi (Y\mid X)$, where $Y$ is a scalar response variable and $X$ is a vector of predictors. This approach is motivated by the fact that in many situations the marginal predictor distribution $\pi (X)$ can provide useful information about the parameter values governing the conditional regression. However, under very mild misspecification, this marginal distribution can also lead conditional inferences astray. Here, we explore these ideas in the context of linear factor models, to understand how they play out in a familiar setting. The resulting Bayesian model performs well across a wide range of covariance structures, on real and simulated data.

16 pages, 1 figure, 2 tables

References in corpus (2)

• Fisher Lecture: Dimension Reduction in Regression
• Sparse covariance thresholding for high-dimensional variable selection