Predictor-dependent shrinkage for linear regression via partial factor modeling

Details Predictor-dependent shrinkage for linear regression via partial factor modeling Predictor-dependent shrinkage for linear regression via partial factor modeling

2010-11-16

arxiv.org/abs/1011.3725v1

Statistics

Methodology

16 pages, 1 figure, 2 tables

Scientific paper

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.

Affiliated with

Also associated with

No associations

Rating

Predictor-dependent shrinkage for linear regression via partial factor modeling does not yet have a rating. At this time, there are no reviews or comments for this scientific paper.

If you have personal experience with Predictor-dependent shrinkage for linear regression via partial factor modeling, we encourage you to share that experience with our LandOfFree.com community. Your opinion is very important and Predictor-dependent shrinkage for linear regression via partial factor modeling will most certainly appreciate the feedback.

Rate now

Comments { 0 }

Profile ID: LFWR-SCP-O-464731