Comparison of different calibration methods suited for calibration problems with many variables

Helle Holst

    Research output: Contribution to journalJournal articleResearchpeer-review

    Abstract

    This paper describes and compares different kinds of statistical methods proposed in the literature as suited for solving calibration problems with many variables. These are: principal component regression, partial least-squares, and ridge regression. The statistical techniques themselves do not provide robust results in the spirit of calibration equations which can last for long periods. A way of obtaining this property is by smoothing and differentiating the data. These techniques are considered, and it is shown how they fit into the treated description.
    Original languageEnglish
    JournalApplied Spectroscopy
    Volume46
    Issue number12
    Pages (from-to)1780-1784
    ISSN0003-7028
    DOIs
    Publication statusPublished - 1992

    Fingerprint Dive into the research topics of 'Comparison of different calibration methods suited for calibration problems with many variables'. Together they form a unique fingerprint.

    Cite this