A beta partial least squares regression model: Diagnostics and application to mining industry data. (29th September 2017)

• Record Type: Journal Article
• Authors: Huerta, Mauricio; Leiva, Víctor; Lillo, Camilo; Rodríguez, Marcelo
• Other Names: Nicolis Orietta guestEditor.; Pascual Rodrigo guestEditor.
• Is Part Of: Applied stochastic models in business and industry. Volume 34:Number 3(2018)
• Page Start: 305
• Page End: 321
• Publication Date: 2017-09-29
• Subjects: Cook distance -- generalized linear models -- Mahalanobis distance -- NIR spectra data -- principal component analysis -- quantile residual -- R software; Stochastic analysis -- Periodicals; Stochastic processes -- Periodicals; Business mathematics -- Periodicals; Finance -- Mathematical models -- Periodicals; Industrial management -- Mathematical models -- Periodicals; 338.00151923
• DOI: 10.1002/asmb.2278
• Languages: English
• ISSNs: 1524-1904

Abstract

Abstract : We propose a methodology based on partial least squares (PLS) regression models using the beta distribution, which is useful for describing data measured between zero and one. The beta PLS model parameters are estimated with the maximum likelihood method, whereas a randomized quantile residual and the generalized Cook and Mahalanobis distances are considered as diagnostic methods. A simulation study is provided for evaluating the performance of these diagnostic methods. We illustrate the methodology with real‐world mining data. The results obtained in this study based on the beta PLS model and its diagnostics may be of interest for the mining industry.