Sparse Partial Least Squares Regression for Simultaneous Dimension Reduction and Variable Selection

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ID: 302119
2010
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Ranked #140 of 145 articles by views in Journal of the Royal Statistical Society Series B (Statistical Methodology)

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Abstract
Partial least squares regression has been an alternative to ordinary least squares for handling multicollinearity in several areas of scientific research since the 1960s. It has recently gained much attention in the analysis of high dimensional genomic data. We show that known asymptotic consistency of the partial least squares estimator for a univariate response does not hold with the very large p and small n paradigm. We derive a similar result for a multivariate response regression with partial least squares. We then propose a sparse partial least squares formulation which aims simultaneously to achieve good predictive performance and variable selection by producing sparse linear combinations of the original predictors. We provide an efficient implementation of sparse partial least squares regression and compare it with well-known variable selection and dimension reduction approaches via simulation experiments. We illustrate the practical utility of sparse partial least squares regression in a joint analysis of gene expression and genomewide binding data.
Reference Key
openalex_W2170917242 Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Hyonho Chun, Sündüz Keleş
Journal Journal of the Royal Statistical Society Series B (Statistical Methodology)
Year 2010
DOI
10.1111/j.1467-9868.2009.00723.x
URL
https://onlinelibrary.wiley.com/doi/pdfdirect/10.1111/j.1467-9868.2009.00723.x
Keywords Keywords not found

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