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

Mauricio Huerta, Víctor Leiva, Carolina Marchant, Marcelo Rodríguez

Resultado de la investigación: Capítulo del libro/informe/acta de congresoContribución a la conferenciarevisión exhaustiva

1 Cita (Scopus)

Resumen

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.

Idioma originalInglés
Título de la publicación alojadaProceedings of the 13th International Conference on Management Science and Engineering Management, 2019 - Volume 1
EditoresJiuping Xu, Syed Ejaz Ahmed, Fang Lee Cooke, Gheorghe Duca
EditorialSpringer Verlag
Páginas470-495
Número de páginas26
ISBN (versión impresa)9783030212476
DOI
EstadoPublicada - 2020
Publicado de forma externa
Evento13th International Conference on Management Science and Engineering Management, ICMSEM 2019 - St. Catharines, Canadá
Duración: 5 ago 20198 ago 2019

Serie de la publicación

NombreAdvances in Intelligent Systems and Computing
Volumen1001
ISSN (versión impresa)2194-5357
ISSN (versión digital)2194-5365

Conferencia

Conferencia13th International Conference on Management Science and Engineering Management, ICMSEM 2019
País/TerritorioCanadá
CiudadSt. Catharines
Período5/08/198/08/19

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