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

Resultado de la investigación: Contribución a una revistaArtículorevisión exhaustiva

4 Citas (Scopus)

Resumen

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.

Idioma originalInglés
Número de artículo104328
PublicaciónChemometrics and Intelligent Laboratory Systems
Volumen214
DOI
EstadoPublicada - 15 jul. 2021
Publicado de forma externa

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