Estimating the covariance matrix of the coefficient estimator in multivariate partial least squares regression with chemical applications

José L. Martínez, VICTOR ELISEO LEIVA SANCHEZ, Helton Saulo, Shuangzhe Liu

Research output: Contribution to journalArticlepeer-review

Abstract

The partial least squares (PLS) regression is a statistical learning technique that solves collinearity and/or high-dimensionality in the space of covariates. In this paper, we propose a new estimator for the covariance matrix of the estimator of the regression coefficients in the multivariate PLS model. This new estimator is simple to be calculated and with a low computational cost. We conduct a Monte Carlo simulation study to assess the performance of the proposed estimator. Then, we apply our proposal to analyze a multivariate real chemical data set. These numerical results show the excellent performance of our proposal.

Original languageEnglish
Article number104328
JournalChemometrics and Intelligent Laboratory Systems
Volume214
DOIs
StatePublished - 15 Jul 2021
Externally publishedYes

Keywords

  • Covariance matrix
  • Jackknife method
  • Monte Carlo method
  • PLS regression
  • R software
  • Standard error

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