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
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