Performance comparison between expanded uncertainty evaluation algorithms

Ye Chow Kuang, Melanie Po-Leen Ooi, Arvind Rajan

    Research output: Chapter in Book/Report/Conference proceedingConference PaperResearchpeer-review

    2 Citations (Scopus)


    The use of normal approximation to estimate expanded uncertainty has been very widespread; yet this is one of the practices that is being criticized by various quarters for lack of rigor and potentially misleading. Monte Carlo method is probably the only method trusted to generate reliable expanded uncertainty. Unfortunately, Monte Carlo method is not applicable for type-A evaluations. This is one of the challenges faced by current researchers in measurement community. This paper presents the comparison of expanded uncertainty estimation accuracy between Monte Carlo method, normal approximation and four well-known moment based distribution fitting methods. The Cornish-Fisher approximation is found to be consistently better than normal approximation but none of the moment based approach is comparable to Monte Carlo method in terms of accuracy and consistency.
    Original languageEnglish
    Title of host publicationProceedings of the 2015 IEEE International Instrumentation and Measurement Technology Conference
    EditorsMarcantonio Catelani
    Place of PublicationNew Jersey USA
    PublisherIEEE, Institute of Electrical and Electronics Engineers
    Pages1729 - 1734
    Number of pages6
    ISBN (Print)9781479961139
    Publication statusPublished - 2015
    EventIEEE International Instrumentation and Measurement Technology Conference 2015 - Pisa, Italy
    Duration: 11 May 201514 May 2015
    Conference number: 32nd (Proceedings)


    ConferenceIEEE International Instrumentation and Measurement Technology Conference 2015
    Abbreviated titleI2MTC 2015
    OtherTheme: The "Measureable" of Tomorrow: Providing a Better Perspective on Complex Systems
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