scholarly journals Using decision rule in calibration of long gauge block on the conformity assessment scheme

Author(s):  
M H Habibie ◽  
O Hedrony
2021 ◽  
Vol 854 (1) ◽  
pp. 012093
Author(s):  
Silvana Stajkovic ◽  
Dragan Vasilev ◽  
Mirjana Dimitrijevic ◽  
Nedjeljko Karabasil

Abstract Knowledge of the measurement uncertainty of test results is fundamentally important for laboratories, their customers and all parties using and interpreting these results. In conformity assessment, a measurement result is used to decide if an item of interest conforms to a specified requirement. Because of measurement uncertainty, there is always the risk of incorrectly deciding whether or not an item conforms to a specified requirement based on the measured value of a property of the item. Conformity assessment can be quite challenging when the entity measured is so close to the tolerance limits of the specification that its uncertainty, however estimated, critically affects decision-making. In such cases, different decision rules can be used to make statements of conformity. The aim of this paper is to provide a survey of methods for the evaluation of measurement uncertainty in testing, as well as to stress the need for appropriate estimation of measurement uncertainty. This paper also aims to assist testing laboratories in understanding the different decision rules used in conformity assessment and level of risk (such as false accept and false reject) associated with the decision rule employed.


Author(s):  
Alexandre Allard ◽  
Nicolas Fischer ◽  
Ian Smith ◽  
Peter Harris ◽  
Leslie Pendrill

In 2012, the Joint Committee for Guides in Metrology (JCGM) published novel guidance on the consideration of measurement uncertainty for decision-making in conformity assessment (JCGM 106:2012). The two situations of making a wrong decision are considered: the risk of accepting a non-conforming item, denoted as the customer risk, and the risk of rejecting a conforming item, denoted as the producer risk. In 2017, the revision of ISO 17025 obliged calibration and testing laboratories to “document the decision rule employed, taking into account the level of risk (such as false accept and false reject and statistical assumptions) associated with the decision rule employed, and apply the decision rule” in the context of the decision made about the conformity of an item. However, JCGM 106:2012 can in some cases be perceived as quite difficult to apply for non-statisticians as it mainly relies on calculations involving probability distributions. In order to facilitate uptake of the methodology of JCGM 106:2012, EURAMET is funding the project EMPIR 17SIP05 “CASoft” (2018 – 2020), involving the National Measurement Institutes from France, Sweden and the UK. The objective is to make the methodology accessible to organisations involved in decision-making in conformity assessment: calibration and testing laboratories, industrialists and regulation authorities. Where the customer or producer are concerned, there are two kinds of risks arising from measurement uncertainty: specific risk which concerns the risk of an incorrect decision for a particular item and global risk which is the risk of an incorrect decision for any item chosen at random. Both kinds of risk may involve prior information, taken into account through a so-called prior probability distribution, introducing the concept of a Bayesian evaluation of the risks. If a calibration and testing laboratory performing the measurement has difficulty accessing prior information, it is likely that the industrialist in control of production processes will have some idea of the quality of the items produced. In this paper, the two problems of estimating the specific and global risks are addressed. The consideration of prior information is also discussed through a practical example as well as the use of software implementing the methodology, which will be made publically available at the end of the project.


1997 ◽  
Vol 78 (02) ◽  
pp. 794-798 ◽  
Author(s):  
Bowine C Michel ◽  
Philomeen M M Kuijer ◽  
Joseph McDonnell ◽  
Edwin J R van Beek ◽  
Frans F H Rutten ◽  
...  

Summary Background: In order to improve the use of information contained in the medical history and physical examination in patients with suspected pulmonary embolism and a non-high probability ventilation-perfusion scan, we assessed whether a simple, quantitative decision rule could be derived for the diagnosis or exclusion of pulmonary embolism. Methods: In 140 consecutive symptomatic patients with a non- high probability ventilation-perfusion scan and an interpretable pulmonary angiogram, various clinical and lung scan items were collected prospectively and analyzed by multivariate stepwise logistic regression analysis to identify the most informative combination of items. Results: The prevalence of proven pulmonary embolism in the patient population was 27.1%. A decision rule containing the presence of wheezing, previous deep venous thrombosis, recently developed or worsened cough, body temperature above 37° C and multiple defects on the perfusion scan was constructed. For the rule the area under the Receiver Operating Characteristic curve was larger than that of the prior probability of pulmonary embolism as assessed by the physician at presentation (0.76 versus 0.59; p = 0.0097). At the cut-off point with the maximal positive predictive value 2% of the patients scored positive, at the cut-off point with the maximal negative predictive value pulmonary embolism could be excluded in 16% of the patients. Conclusions: We derived a simple decision rule containing 5 easily interpretable variables for the patient population specified. The optimal use of the rule appears to be in the exclusion of pulmonary embolism. Prospective validation of this rule is indicated to confirm its clinical utility.


Author(s):  
Michael Laver ◽  
Ernest Sergenti

This chapter extends the survival-of-the-fittest evolutionary environment to consider the possibility that new political parties, when they first come into existence, do not pick decision rules at random but instead choose rules that have a track record of past success. This is done by adding replicator-mutator dynamics to the model, according to which the probability that each rule is selected by a new party is an evolving but noisy function of that rule's past performance. Estimating characteristic outputs when this type of positive feedback enters the dynamic model creates new methodological challenges. The simulation results show that it is very rare for one decision rule to drive out all others over the long run. While the diversity of decision rules used by party leaders is drastically reduced with such positive feedback in the party system, and while some particular decision rule is typically prominent over a certain period of time, party systems in which party leaders use different decision rules are sustained over substantial periods.


Author(s):  
Michael Laver ◽  
Ernest Sergenti

This chapter attempts to develop more realistic and interesting models in which the set of competing parties is a completely endogenous output of the process of party competition. It also seeks to model party competition when different party leaders use different decision rules in the same setting by building on an approach pioneered in a different context by Robert Axelrod. This involves long-running computer “tournaments” that allow investigation of the performance and “robustness” of decision rules in an environment where any politician using any rule may encounter an opponent using either the same decision rule or some quite different rule. The chapter is most interested in how a decision rule performs against anything the competitive environment might throw against it, including agents using decision rules that are difficult to anticipate and/or comprehend.


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