PLS model building with missing data: New algorithms and a comparative study

New algorithms to deal with missing values in predictive modelling are presented in this article. Specifically, 2 trimmed scores regression adaptations are proposed, one from principal component analysis model building with missing data (MD) and other from partial least squares regression model exploitation with missing values. Using these methods, practitioners can impute MD both in the explanatory/predictor and the dependent/response variables. Partial least squares is used here to build the multivariate calibration models; however, any regression method can be used after MD imputation. Four case studies, with different latent structures, are analysed here to compare the trimmed scores regression-based methods against state-of-the-art approaches. The MATLAB code for these methods is also provided for its direct implementation at , under a GNU license. New algorithms to deal with missing values in predictive modelling are presented here. Specifically, two trimmed scores regression (TSR) adaptations are proposed, one from PCA model building with missing data (MD) and other from PLS regression model exploitation with MD. Using these methods, practitioners can impute MD both in the explanatory/predictor and the dependent/response variables. TSR is compared to other state-of-the-art methods in four case studies. The MATLAB code for TSR methods is freely available.