A beta partial least squares regression model: Diagnostics and application to mining industry data

Mauricio Huerta, VICTOR ELISEO LEIVA SANCHEZ, Camilo Lillo, Marcelo Rodríguez

Research output: Contribution to journalArticlepeer-review

10 Scopus citations

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.

Original languageEnglish
Pages (from-to)305-321
Number of pages17
JournalApplied Stochastic Models in Business and Industry
Volume34
Issue number3
DOIs
StatePublished - 1 May 2018

Keywords

  • Cook distance
  • generalized linear models
  • Mahalanobis distance
  • NIR spectra data
  • principal component analysis
  • quantile residual
  • R software

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