H. Akaike, Fitting autoregressive models for prediction, Ann. Ins. Stat. Math, vol.21, pp.243-247, 1969.

F. Arteaga and A. Ferrer, Dealing with missing data in MSPC: Several methods, different interpretations, some examples, J. Chemom, vol.16, pp.408-418, 2002.

M. J. Azur, E. A. Stuart, C. Frangakis, and P. J. Leaf, Multiple imputation by chained equations: what is it and how does it work?, Int. J. Methods Psychiatr. Res, vol.20, pp.40-49, 2011.

P. Bastien, M. Tenenhaus, ;. Tenenhaus, M. Coelho, P. Vinzi et al., PLS regression and multiple imputation, Proceedings of the PLS'03 International Symposium, Vilares, M, pp.497-498, 2003.

F. Bertrand, N. Meyer, and M. Maumy-bertrand, plsRglm: partial least squares regression for generalized linear models, book of abstracts, 2014.

T. E. Bodner, What improves with increased missing data imputations?, Structur. Equ. Modeling, vol.15, pp.651-675, 2008.

A. J. Burnham, R. Viveros, and J. F. Macgregor, Frameworks for latent variable multivariate regression, J. Chemom, vol.10, pp.31-45, 1996.

A. J. Burnham, J. F. Macgregor, and R. Viveros, Latent variable multivariate regression modeling, Chemom. Intell. Lab. Syst, vol.48, pp.167-180, 1999.

D. Jong and S. , SIMPLS: an alternative approach squares regression to partial least, Chemom. Intell. Lab. Syst, vol.18, pp.251-263, 1993.

J. K. Dixon, Pattern recognition with partly missing data, IEEE Trans. Syst. Man Cybern, vol.10, pp.617-621, 1979.

H. T. Eastment and W. J. Krzanowski, Cross-validatory choice of the number of components from a principal component analysis, Technometrics, vol.24, pp.73-77, 1982.

I. Eriksson, E. Johansson, N. Kettaneh-wold, and S. Wold, Multi-and megavariate data analysis, principles and applications, J. Chemom, vol.16, pp.261-262, 2002.

A. Folch-fortuny, F. Arteaga, and A. Ferrer, Missing data imputation toolbox for MATLAB, Chemom. Intell. Lab. Syst, vol.154, pp.93-100, 2016.

H. C. Goicoechea and A. C. Olivieri, Determination of bromhexine in cough-cold syrups by absorption spectrophotometry and multivariate calibration using partial least-squares and hybrid linear analyses. Application of a novel method of wavelength selection, Talanta, vol.49, pp.793-800, 1999.

H. C. Goicoechea and A. C. Olivieri, Enhanced synchronous spectrofluorometric determination of tetracycline in blood serum by chemometric analysis. Comparison of partial least-squares and hybrid linear analysis calibrations, Anal. Chem, vol.71, pp.4361-4368, 1999.

H. C. Goicoechea and A. C. Olivieri, A new family of genetic algorithms for wavelength interval selection in multivariate analytical spectroscopy, J. Chemom, vol.17, pp.338-345, 2003.

J. W. Graham, A. E. Olchowski, and T. D. Gilreath, How many imputations are really needed? Some practical clarifications of multiple imputation theory, Prev. Sci, vol.8, pp.206-213, 2007.

B. Grung and R. Manne, Missing values in principal component analysis, Chemom. Intell. Lab. Syst, vol.42, pp.125-139, 1998.

N. J. Horton and S. R. Lipsitz, Multiple imputation in practice: Comparison of software packages for regression models with missing variables, Am. Stat, vol.55, pp.244-254, 2001.

A. Höskuldsson, PLS regression, J. Chemom, vol.2, pp.211-228, 1988.

A. Kowarik and M. Templ, Imputation with the R package VIM, J. Stat. Softw, vol.74, pp.1-16, 2016.

N. Krämer and M. L. Braun, plsdof: degrees of freedom and statistical inference for partial least squares regression, 2015.

N. Krämer and M. Sugiyama, The degrees of freedom of partial least squares regression, J. Am. Stat. Assoc, vol.106, pp.697-705, 2012.

O. Kvalheim, The latent variable, Chemom. Intell. Lab. Syst, vol.14, pp.1-3, 1992.

A. Lazraq, R. Cléroux, and J. Gauchi, Selecting both latent and explanatory variables in the PLS1 regression model, Chemom. Intell. Lab. Syst, vol.66, pp.117-126, 2003.

F. Leisch and E. Dimitriadou, mlbench: Machine Learning Benchmark Problems. R package version, vol.2, pp.1-1, 2010.

B. Li, J. Morris, and E. B. Martin, Model selection for partial least squares regression, Chemome. Intell. Lab. Syst, vol.64, pp.79-89, 2002.

R. J. Little and D. B. Rubin, Statistical analysis with missing data, 1987.

R. J. Little and D. B. Rubin, Statistical analysis with missing data, 2002.

N. Meyer, M. Maumy-bertrand, and F. Bertrand, Comparaison de variantes de régressions logistiques PLS et de régression PLS sur variables qualitatives: application aux données d'allélotypage, J. Soc. Stat. Paris, vol.151, pp.1-18, 2010.

P. R. Nelson, P. A. Taylor, and J. F. Macgregor, Missing data methods in PCA and PLS: score calculations with incomplete observations, Chemom. Intell. Lab. Syst, vol.35, pp.45-65, 1996.

D. V. Nguyen and D. M. Rocke, On partial least squares dimension reduction for microarray-based classification: a simulation study, Comput. Stat. Data An, vol.46, pp.407-425, 2004.

A. Oleszko, J. Hartwich, A. Wójtowicz, M. Ga?ior-g?ogowska, H. Huras et al., Comparison of FTIR-ATR and Raman spectroscopy in determination of VLDL triglycerides in blood serum with PLS regression, Spectrochim. Acta A Mol. Biomol. Spectrosc, vol.183, pp.239-246, 2017.

M. Pérez-enciso and M. Tenenhaus, Prediction of clinical outcome with microarray data: a partial least squares discriminant analysis (PLS-DA) approach Received, Hum. Genet, vol.112, pp.581-592, 2003.

P. O. Perry, bcv: Cross-validation for the SVD, 2015.

S. Rännar, P. Geladi, F. Lindgren, and S. Wold, A PLS Kernel algorithm for data sets with many variables and few objects. 2. Crossvalidataion, missing data and examples, J. Chemom, vol.9, pp.459-470, 1995.

R. Rosipal and N. Krämer, Overview and recent advances in partial least squares, Subspace, Latent Structure and Feature Selection, Statistical and Optimization, pp.34-51, 2005.

P. Royston, Multiple imputation of missing values, Stata J, vol.4, pp.227-241, 2004.

D. B. Rubin, Multiple imputation for nonresponse in surveys, 1987.

D. B. Rubin, Multiple imputation after 18+ years, J. Am. Stat. Assoc, vol.91, pp.473-489, 1996.

M. L. Sawatsky, M. Clyde, and F. Meek, Partial least squares regression in the social sciences, Quant. Method Psychol, vol.11, pp.52-62, 2015.

G. Schwarz, Estimating the dimension of a model, Ann. Stat, vol.6, pp.461-464, 1978.

S. Serneels and T. Verdonck, Principal component regression for data containing outliers and missing elements, Comput. Stat. Data An, vol.52, pp.1712-1727, 2008.

M. Stone, Cross-validatory choice and assessment of statistical predictions, J. R. Stat. Soc, vol.36, pp.111-147, 1974.

M. Templ, A. Alfons, A. Kowarik, and B. Prantner, VIM: visualization and imputation of missing values, La Régression PLS: théorie et pratique, Editions Technip, 1998.

O. Troyanskaya, M. Cantor, G. Sherlock, P. Brown, T. Hastie et al., Missing value estimation methods for DNA microarrays, Bioinformatics, vol.17, pp.520-525, 2001.

S. Van-buuren, Multiple imputation of discrete and continuous data by fully conditional specification, Stat. Methods Med. Res, vol.16, pp.219-242, 2007.

S. Van-buuren, Flexible imputation of missing data, mice: Multivariate imputation by chained equations, 2012.

S. Van-buuren and K. Groothuis-oudshoorn, A test of significance for partial least squares regression, J. Stat. Softw, vol.45, pp.291-304, 1993.

I. R. White, P. Royston, and A. M. Wood, Multiple imputation using chained equations: issues and guidance for practice, Stat. Med, vol.30, pp.377-399, 2011.

S. Wiklund, D. Nilsson, L. Eriksson, M. Sjöström, S. Wold et al., A randomization test for PLS component selection, J. Chemom, vol.21, pp.427-439, 2007.

H. Wold, S. Wold, K. Esbensen, and P. Geladi, Estimation of principal components and related models by iterative least squares, Chemom. Intell. Lab. Syst, vol.1, pp.37-52, 1966.

S. Wold, M. Sjöström, and L. Eriksson, PLS-regression: a basic tool of chemometrics, Chemom. Intell. Lab. Syst, vol.58, pp.109-130, 2001.

T. C. Yang, L. S. Aucott, G. G. Duthie, and H. M. Macdonald, An application of partial least squares for identifying dietary patterns in bone health, Arch. osteoporosis, vol.12, p.63, 2017.