@article{e44ccf3f91814eddb5b894786e3b3580,
title = "Estimating the covariance matrix of the coefficient estimator in multivariate partial least squares regression with chemical applications",
abstract = "The partial least squares (PLS) regression is a statistical learning technique which solves the problems of 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 our proposed estimator. Then, we apply our results to analyze a multivariate real chemical data set. These numerical results show the good performance of our proposal.",
keywords = "Covariance matrix, Jackknife method, Monte Carlo method, PLS regression, R software, Standard error",
author = "Mart{\'i}nez, {Jos{\'e} L.} and V{\'i}ctor Leiva and Helton Saulo and Shuangzhe Liu",
note = "Funding Information: The authors would like to thank the Editors and Reviewers very much for their constructive comments on an earlier version of this manuscript which resulted in this improved version. The research of H. Saulo was partially supported by the National Council for Scientific and Technological Development (CNPq) and the Coordination for the Improvement of Higher Education Personnel (CAPES) from the Brazilian federal government . The research of V. Leiva was partially supported by grant FONDECYT 1200525 from the National Agency for Research and Development (ANID) of the Chilean government under the Ministry of Science, Technology, Knowledge and Innovation . Publisher Copyright: {\textcopyright} 2021 Elsevier B.V.",
year = "2021",
month = jul,
day = "15",
doi = "10.1016/j.chemolab.2021.104328",
language = "English",
volume = "214",
pages = "1--8",
journal = "Chemometrics and Intelligent Laboratory Systems",
issn = "0169-7439",
publisher = "Elsevier",
}