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 language | English |
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Article number | 104328 |
Journal | Chemometrics and Intelligent Laboratory Systems |
Volume | 214 |
DOIs | |
State | Published - 15 Jul 2021 |
Keywords
- Covariance matrix
- Jackknife method
- Monte Carlo method
- PLS regression
- R software
- Standard error