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dc.contributor.authorHuerta, Mauricio
dc.contributor.authorLeiva, Víctor
dc.contributor.authorMarchant-Fuentes, Carolina
dc.contributor.authorRodríguez-Gallardo, Marcelo
dc.description.abstractPartial 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.es_CL
dc.rightsAtribución-NoComercial-SinDerivadas 3.0 Chile*
dc.sourceAdvances in Intelligent Systems and Computing, 1001, 470-495es_CL
dc.subjectCook distancees_CL
dc.subjectLinear modelses_CL
dc.subjectMahalanobis distancees_CL
dc.subjectNIR spectra dataes_CL
dc.subjectPrincipal component analysises_CL
dc.subjectQuantile residualses_CL
dc.subjectR softwarees_CL
dc.titlePartial least squares models and their formulations, diagnostics and applications to spectroscopyes_CL

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Atribución-NoComercial-SinDerivadas 3.0 Chile
Except where otherwise noted, this item's license is described as Atribución-NoComercial-SinDerivadas 3.0 Chile