Scaled Predictor Envelopes and Partial Least Squares Regression

• Activity Number: 673
• Type: Topic Contributed
• Date/Time: Thursday, August 13, 2015 : 10:30 AM to 12:20 PM
• Sponsor: Section on Statistical Learning and Data Mining
• Author(s): Zhihua Su* and Dennis Cook
• Companies: University of Florida and University of Minnesota
• Keywords: Dimension reduction ; Envelope model ; Grassmann manifold ; Partial least squares ; Scale invariance

Abstract

Partial least squares (PLS) is a widely used method for prediction in applied statistics, especially in chemometrics applications. However, PLS is not invariant or equivariant under scale transformations of the predictors, which tends to limit its scope to regressions in which the predictors are measured in the same or similar units. Cook et al. (2013) built a connect between nascent envelope methodology and PLS, allowing PLS to be addressed in a traditional likelihood-based framework. In this article, we use the connection between PLS and envelopes to develop a new method -- scaled predictor envelopes (SPE) -- that incorporates predictor scaling into PLS-type applications. By estimating the appropriate scales, the SPE estimators can offer efficiency gains beyond those given by PLS, and further reduce prediction errors. Simulations and an example are given to support the theoretic claims.