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Hoskuldsson, A. (2001) Variable and Subset Selection in PLS Regression. Chemometrics and Intelligent Laboratory Systems, 557, 23-38.
http://dx.doi.org/10.1016/S0169-7439(00)00113-1

has been cited by the following article:

  • TITLE: Latent Structure Linear Regression

    AUTHORS: Agnar Höskuldsson

    KEYWORDS: H-Principle of Mathematical Modelling; H-Methods; PLS Regression; Latent Structure Regression

    JOURNAL NAME: Applied Mathematics, Vol.5 No.5, March 24, 2014

    ABSTRACT: A short review is given of standard regression analysis. It is shown that the results presented by program packages are not always reliable. Here is presented a general framework for linear regression that includes most linear regression methods based on linear algebra. The H-principle of mathematical modelling is presented. It uses the analogy between the modelling task and measurement situation in quantum mechanics. The principle states that the modelling task should be carried out in steps where at each step an optimal balance should be determined between the value of the objective function, the fit, and the associated precision. H-methods are different methods to carry out the modelling task based on recommendations of the H-principle. They have been applied to different types of data. In general, they provide better predictions than linear regression methods in the literature.