2014/05/31 by L. Lista, Luca Lista · 2 citations
Agricultural and Biological Sciences · Decision Sciences · Mathematics · Physics and Astronomy · #Advanced Statistical Methods and Models #Algorithm #Applied mathematics #Best linear unbiased prediction #Bias of an estimator #Computer science #Efficiency #Estimator #Iterative method #Materials science #Mathematics #Measurement uncertainty #Minimum-variance unbiased estimator #Observable #Pesticide Residue Analysis and Safety #Physics #Range (aeronautics) #Scientific Measurement and Uncertainty Evaluation #Statistics #Stein's unbiased risk estimate #Unbiased Estimation #physics.data-an
paper · pdf · doi:10.1016/j.nima.2014.07.021
published as Nucl.Instrum.Meth. A764 (2014) 82-93; corrig. ibid. A773 (2015) 87-96 · 23 pages, 14 figures, accepted for publication in Nucl. Instr. and Meth. A
openalex publication_date 2014/07/15 · arxiv created 2015/01/16 · arxiv updated 2015/01/19 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/15
The best linear unbiased estimator (BLUE) is a popular statistical method adopted to combine multiple measurements of the same observable taking into account individual uncertainties and their correlation. The method is unbiased by construction if the true uncertainties and their correlation are known, but it may exhibit a bias if uncertainty estimates are used in place of the true ones, in particular if those estimated uncertainties depend on measured values. This is the case for instance when contributions to the total uncertainty are known as relative uncertainties. In those cases, an iterative application of the BLUE method may reduce the bias of the combined measurement. The impact of the iterative approach compared to the standard BLUE application is studied for a wide range of possible values of uncertainties and their correlation in the case of the combination of two measurements.