@inproceedings{16c51f8bef434595a24bd2debc9561e3,
title = "Partial least squares models and their formulations, diagnostics and applications to spectroscopy",
abstract = "Partial least squares (PLS) models are a multivariate technique developed to solve the problem of multicollinearity and/or high dimensionality related to explanatory variables in multiple linear models. PLS models have been extensively applied assuming normality, but this assumption is not always fulfilled. For example, if the response variable has an asymmetric distribution or it is bounded into an interval, normality is violated. In this work, we present a collection of PLS models and their formulations, diagnostics and applications. Formulations are based on different symmetric, asymmetric and bounded distributions, such as normal, beta and Birnbaum-Saunders. Diagnostics are based on residuals and the Cook and Mahalanobis distances. Applications are provided using real-world spectroscopy data.",
keywords = "Cook distance, Linear models, Mahalanobis distance, NIR spectra data, Principal component analysis, Quantile residuals, R software",
author = "Mauricio Huerta and V{\'i}ctor Leiva and Carolina Marchant and Marcelo Rodr{\'i}guez",
note = "Funding Information: Acknowledgement. The authors thank the editors and reviewers for their comments on this manuscript. This research work was partially supported by FONDECYT 1160868 grant from the Chilean government. Publisher Copyright: {\textcopyright} Springer Nature Switzerland AG 2020.; null ; Conference date: 05-08-2019 Through 08-08-2019",
year = "2020",
doi = "10.1007/978-3-030-21248-3_35",
language = "English",
isbn = "9783030212476",
series = "Advances in Intelligent Systems and Computing",
publisher = "Springer Verlag",
pages = "470--495",
editor = "Jiuping Xu and Ahmed, {Syed Ejaz} and Cooke, {Fang Lee} and Gheorghe Duca",
booktitle = "Proceedings of the 13th International Conference on Management Science and Engineering Management, 2019 - Volume 1",
}