Uncertainty estimation for multivariate regression coefficients

N.M. Faber

    Research output: Contribution to journalArticleAcademicpeer-review

    42 Citations (Scopus)

    Abstract

    Five methods are compared for assessing the uncertainty in multivariate regression coefficients, namely, an approximate variance expression and four resampling methods (jack-knife, bootstrapping objects, bootstrapping residuals, and noise addition). The comparison is carried out for simulated as well as real near-infrared data. The calibration methods considered are ordinary least squares (simulated data), partial least squares regression, and principal component regression (real data). The results suggest that the approximate variance expression is a viable alternative to resampling.
    Original languageEnglish
    Pages (from-to)169-179
    JournalChemometrics and Intelligent Laboratory Systems
    Volume64
    Issue number2
    DOIs
    Publication statusPublished - 2002

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