Partial least squares models and their formulations, diagnostics and applications to spectroscopy

Author
Huerta, Mauricio
Leiva, Víctor
Marchant-Fuentes, Carolina
Rodríguez-Gallardo, Marcelo
Date
2020Metadata
Show full item recordAbstract
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.
Fuente
Advances in Intelligent Systems and Computing, 1001, 470-495Identificador DOI
doi.org/10.1007/978-3-030-21248-3_35Collections
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