Uncertainty Analysis for Vector Measurands Using Fiducial Inference
Chih-Ming Wang, Hariharan K. Iyer
This paper presents a method for constructing uncertainty regions for a vector measurand in the presence of both type-A and type-B errors. The method is based on the principle of fiducial inference and generally requires a Monte Carlo approach for computing uncertainty regions. A small simulation study is carried out to evaluate the performance of the method. Computer programs, written in public-domain software, for computing uncertainty regions are listed. An example, involving complex S-parameter measurements, is used to illustrate the proposed method.
generalized pivotal quantities, ISO GUM, key comparisons.