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Three Statistical Paradigms for the Assessment and Interpretation of Measurement Uncertainty

Published

Author(s)

William F. Guthrie, Hung-Kung Liu, Andrew L. Rukhin, Blaza Toman, Chih-Ming Wang, Nien F. Zhang

Abstract

The goals of this chapter are to present different approaches to uncertainty assessment from a statistical point of view and to relate them to the methods that are currently being used in metrology or are being developed within the metrology community. The particular statistical paradigms under which different methods for uncertainty assessment will be described include the frequentist, Bayesian, and fiducial paradigms.
Citation
Book chapter in Advances in Data Modeling for Measurements i

Keywords

uncertainty analysis, frequentist, Bayesian, fiducial, metrology, statistics

Citation

Guthrie, W. , Liu, H. , Rukhin, A. , Toman, B. , Wang, C. and Zhang, N. (2009), Three Statistical Paradigms for the Assessment and Interpretation of Measurement Uncertainty, Book chapter in Advances in Data Modeling for Measurements i (Accessed July 25, 2024)

Issues

If you have any questions about this publication or are having problems accessing it, please contact reflib@nist.gov.

Created January 12, 2009, Updated January 27, 2020