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Activity Number: 56 - Novel Statistical Methods for Variable Selection with Applications
Type: Topic Contributed
Date/Time: Sunday, July 29, 2018 : 4:00 PM to 5:50 PM
Sponsor: Section on Nonparametric Statistics
Abstract #330010 Presentation
Title: Partial Least Square: Theoretical Results for the Chemometrics Use of PLS
Author(s): Liliana Forzani* and Dennis Cook
Companies: FACULTAD DE INGENIERIA QUIMICA and School of Statistics
Keywords: partial least square; p larger than n

Partial least squares (PLS) is one of the first methods for prediction in high-dimensional linear regressions in which the sample size need not be large relative to the number of predictors. Since its development, PLS regression has taken place mainly within the chemometrics community, where empirical prediction is the main issue, but PLS is now a core method for big data. However, studies of PLS have appeared in mainline statistics literature only from time to time and there have been no positive results on the theoretical properties of the chemometrics community's use of PLS. In a joint work with R. Dennis Cook we study the theoretical properties of prediction using PLS in the same context that chemometrics community use.

Authors who are presenting talks have a * after their name.

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