Abstract
This paper discusses two approaches to the reconciliation of joint measurements with non-Gaussian errors’ distributions based on considering the functional relationships between measurands: semi-nonparametric based on polynomial approximation or Gram-Charlier series expansions and completely nonparametric based on kernel density estimator. The article presents mathematical expressions for all proposed methods in detail and the results of diverse statistical modelling for determining the limits of their possible applicability. We compared the effectiveness of the mentioned methods in a series of Monte Carlo experiments and showed that the nonparametric approach based on the kernel density estimation is less demanding on sample size than Gram-Charlier approximation for samples of size n > 30. However, in the case of asymmetric densities (skewness greater than 2.0), the reconciling algorithm with Gram-Charlier approximation is preferable for samples of medium size. The paper shows that the recommended measurement amount should be n > 150 for each measured quantity if we use not more than four terms in Gram-Charlier expansion.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
