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  • 1
    Electronic Resource
    Electronic Resource
    New York, NY : Wiley-Blackwell
    Journal of Chemometrics 1 (1987), S. 185-196 
    ISSN: 0886-9383
    Keywords: Cross-validation ; Partial least squares ; Two-sample location ; Chemistry ; Analytical Chemistry and Spectroscopy
    Source: Wiley InterScience Backfile Collection 1832-2000
    Topics: Chemistry and Pharmacology
    Notes: A method for statistical analysis of two independent samples with respect to difference in location is investigated. The method uses the partial least squares projections to latent structures (PLS) with cross-validation. The relation to classical methods is discussed and a Monte Carlo study is performed to describe how the distribution of the test-statistic employed depends on the number of objects, the number of variables, the percentage variance explained by the first PLS-component and the percentage missing values. Polynomial approximations for the dependency of the 50 per cent and the 5 per cent levels of the test-statistic on these factors are given. The polynomial for the 50 per cent level is complicated, involving several first-, second- and third-degree terms, whereas the polynomial for the 5 per cent level is dependent only on the number of objects and the size of the first component. A separate Monte Carlo experiment indicates that a moderate difference in sample size does not affect the distribution of the test-statistic. The multi-sample location problem is also studied and the effect of increasing the number of samples on the test-statistic is shown in simulations.
    Additional Material: 3 Ill.
    Type of Medium: Electronic Resource
    Library Location Call Number Volume/Issue/Year Availability
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  • 2
    Electronic Resource
    Electronic Resource
    New York, NY : Wiley-Blackwell
    Journal of Chemometrics 8 (1994), S. 377-389 
    ISSN: 0886-9383
    Keywords: Kernel PLS regression ; Cross-validation ; Model dimensionality ; Multivariate image regression ; Chemistry ; Analytical Chemistry and Spectroscopy
    Source: Wiley InterScience Backfile Collection 1832-2000
    Topics: Chemistry and Pharmacology
    Notes: Multivariate images are very large data structures and any type of regression for their analysis is very computer-intensive. Kernel-based partial least squares (PLS) regression, presented in an earlier paper, makes the calculation phase more rapid and less demanding in computer memory. The present paper is a direct continuation of the first paper. In this study the kernel PLS algorithm is extended to include cross-validation for determination of the optimal model dimensionality. To show the applicability of the kernel algorithm, two examples from multivariate image analysis are used. The first example is an image from an airborne scanner of size 9 × 512 × 512. It consists of nine images which are regressed against a constructed dependent image to test the accuracy of the kernel algorithm when used on large data structures. The second example is a satellite image of size 7 × 512 × 512. Several different regression models are presented together with a comparison of their predictive capabilities. The regression models are also used as examples for showing the use of cross-validation.
    Additional Material: 3 Ill.
    Type of Medium: Electronic Resource
    Library Location Call Number Volume/Issue/Year Availability
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