• DocumentCode
    1883010
  • Title

    Estimating Uncertainty of a Measurement Process

  • Author

    Chang, Ning ; Lambert, Jim

  • Author_Institution
    Cisco Syst., San Jose
  • fYear
    2007
  • fDate
    27-29 June 2007
  • Firstpage
    9
  • Lastpage
    13
  • Abstract
    Estimating a measurement of software quality is a challenge where uncertainty and variation have the greatest impact. Especially, at times when there is not enough information. Here, EUMP (estimating uncertainty of a measurement process) is introduced. EUMP is a recursive process which is using both multi regression and Monte Carlo simulation. It can be systematically obtained through EUMP for all distribution functions of both dependent and independent variables. Moreover, dependent variable can be estimated. Finally, a predictable system will be described, where the error of an estimated dependent variable will be proven it goes to zero when time (t) goes to infinity.
  • Keywords
    Monte Carlo methods; measurement uncertainty; regression analysis; software quality; EUMP; Monte Carlo simulation; measurement uncertainty; multi regression; software quality; uncertainty estimation; Computational fluid dynamics; Computational intelligence; Distribution functions; Internet; Measurement uncertainty; Predictive models; Recursive estimation; Regression analysis; Software measurement; Software quality; EUMP (Estimating Uncertainty of a Measurement Process); Measurement System; Monte Carlo Simulation; Multi Regression; Predictable System; Probabilistically Converging System;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Computational Intelligence for Measurement Systems and Applications, 2007. CIMSA 2007. IEEE International Conference on
  • Conference_Location
    Ostuni
  • Print_ISBN
    978-1-4244-0824-5
  • Electronic_ISBN
    978-1-4244-0824-5
  • Type

    conf

  • DOI
    10.1109/CIMSA.2007.4362529
  • Filename
    4362529