首页|The GUM perspective on straight-line errors-in-variables regression
The GUM perspective on straight-line errors-in-variables regression
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NSTL
Elsevier
Following the Guide to the expression of uncertainty in measurement (GUM), the slope and intercept in straight-line regression tasks can be estimated and their uncertainty evaluated by defining a measurement model. Minimizing the weighted total least-squares functional appropriately defines such a model when both regression input quantities (X and Y) are uncertain. This paper compares the uncertainty of the straight line evaluated by propagating distributions and by the law of propagation of uncertainty (LPU). The latter is in turn often approximated because the non-linear measurement model does not have closed form. We reason that the uncertainty recommended in the dedicated technical specification ISO/TS 28037:2010 does not fully implement the LPU (as intended) and can understate the uncertainty. A systematic simulation study quantifies this understatement and the circumstances where it becomes relevant. In contrast, the LPU uncertainty may often be appropriate. As a result, it is planned to revise ISO/TS 28037:2010.
Errors-in-variablesStraight-line regressionWeighted total least-squaresLaw of propagation of uncertaintyMonte Carlo methodImplicit measurement modelTOTAL LEAST-SQUARESBAYESIAN UNCERTAINTY ANALYSISALGORITHMMODEL
Klauenberg, Katy、Martens, Steffen、Bosnjakovic, Alen、Cox, Maurice G.、van der Veen, Adriaan M. H.、Elster, Clemens
NSTL
Elsevier
