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

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  • Mauricio Huerta
  • Víctor Leiva
  • Camilo Lillo
  • Marcelo Rodríguez

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.

Suggested Citation

  • Mauricio Huerta & Víctor Leiva & Camilo Lillo & Marcelo Rodríguez, 2018. "A beta partial least squares regression model: Diagnostics and application to mining industry data," Applied Stochastic Models in Business and Industry, John Wiley & Sons, vol. 34(3), pages 305-321, May.
    • Handle: RePEc:wly:apsmbi:v:34:y:2018:i:3:p:305-321
    DOI: 10.1002/asmb.2278
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    Cited by:

    1. Johny Pambabay-Calero & Sergio Bauz-Olvera & Ana Nieto-Librero & Ana Sánchez-García & Puri Galindo-Villardón, 2021. "Hierarchical Modeling for Diagnostic Test Accuracy Using Multivariate Probability Distribution Functions," Mathematics, MDPI, vol. 9(11), pages 1-20, June.

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