New Algorithms for Estimating the Certified Characteristic of CRMs for Substances and Materials Using Interlaboratory Certification
摘要
In this work, algorithms for specifying certified reference materials (CRMs) are developed based on the data modeling of interlaboratory experiments containing hidden uncertainties. These algorithms aid in estimating hidden uncertainties and, thus, in correcting the data and obtaining a consistent value of the certified characteristic. Using the Monte–Carlo method, the properties of hidden uncertainty estimates are studied. The proposed algorithms are compared with those conventionally used.