{"title":"当相对标准不确定度与测量水平无关时,处理偏度的不确定区间表达式","authors":"Eskil Sahlin, Bertil Magnusson","doi":"10.1007/s00769-022-01506-x","DOIUrl":null,"url":null,"abstract":"<div><p>Uncertainty intervals for many measurement results are typically reported as symmetric intervals around the measured value. However, at large standard uncertainties (> approx. 15 %–20 %), it is necessary to consider asymmetry of the uncertainty intervals. Here, an expression for calculating uncertainty intervals handling asymmetry when the relative standard uncertainty is independent of the measurand level is presented. The expression is based on implementation of a power transformation (<span>\\({x}^{B}\\)</span>) for transformation of measurement results in order to achieve results that have a symmetric and approximate normal distribution. Uncertainty intervals are then calculated in the transformed space and back-transformed to the original space. The transformation includes a parameter, <i>B</i>, that needs to be optimized, and this can be based on real results, modelling of results, or on judgement. Two important reference points are <i>B</i> equal to 1 that corresponds to an approximate normal distribution of the original measurement results, and <i>B</i> approaching 0 that corresponds to an approximate log-normal distribution of the original measurement results. Comparisons are made with uncertainty intervals calculated using other expressions where it is assumed that measurement results have a normal distribution or a log-normal distribution. Implementation of the approach is demonstrated with several examples from chemical analysis.</p></div>","PeriodicalId":454,"journal":{"name":"Accreditation and Quality Assurance","volume":"27 4","pages":"223 - 233"},"PeriodicalIF":0.8000,"publicationDate":"2022-07-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s00769-022-01506-x.pdf","citationCount":"0","resultStr":"{\"title\":\"Expression for uncertainty intervals handling skewness when the relative standard uncertainty is independent of the measurand level\",\"authors\":\"Eskil Sahlin, Bertil Magnusson\",\"doi\":\"10.1007/s00769-022-01506-x\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p>Uncertainty intervals for many measurement results are typically reported as symmetric intervals around the measured value. However, at large standard uncertainties (> approx. 15 %–20 %), it is necessary to consider asymmetry of the uncertainty intervals. Here, an expression for calculating uncertainty intervals handling asymmetry when the relative standard uncertainty is independent of the measurand level is presented. The expression is based on implementation of a power transformation (<span>\\\\({x}^{B}\\\\)</span>) for transformation of measurement results in order to achieve results that have a symmetric and approximate normal distribution. Uncertainty intervals are then calculated in the transformed space and back-transformed to the original space. The transformation includes a parameter, <i>B</i>, that needs to be optimized, and this can be based on real results, modelling of results, or on judgement. Two important reference points are <i>B</i> equal to 1 that corresponds to an approximate normal distribution of the original measurement results, and <i>B</i> approaching 0 that corresponds to an approximate log-normal distribution of the original measurement results. Comparisons are made with uncertainty intervals calculated using other expressions where it is assumed that measurement results have a normal distribution or a log-normal distribution. Implementation of the approach is demonstrated with several examples from chemical analysis.</p></div>\",\"PeriodicalId\":454,\"journal\":{\"name\":\"Accreditation and Quality Assurance\",\"volume\":\"27 4\",\"pages\":\"223 - 233\"},\"PeriodicalIF\":0.8000,\"publicationDate\":\"2022-07-16\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://link.springer.com/content/pdf/10.1007/s00769-022-01506-x.pdf\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Accreditation and Quality Assurance\",\"FirstCategoryId\":\"5\",\"ListUrlMain\":\"https://link.springer.com/article/10.1007/s00769-022-01506-x\",\"RegionNum\":4,\"RegionCategory\":\"工程技术\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q4\",\"JCRName\":\"CHEMISTRY, ANALYTICAL\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Accreditation and Quality Assurance","FirstCategoryId":"5","ListUrlMain":"https://link.springer.com/article/10.1007/s00769-022-01506-x","RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"CHEMISTRY, ANALYTICAL","Score":null,"Total":0}
引用次数: 0
摘要
许多测量结果的不确定区间通常报告为测量值周围的对称区间。然而,在很大程度上,标准不确定度(&gt;15%–20 %), it is necessary to consider asymmetry of the uncertainty intervals. Here, an expression for calculating uncertainty intervals handling asymmetry when the relative standard uncertainty is independent of the measurand level is presented. The expression is based on implementation of a power transformation (\({x}^{B}\)) for transformation of measurement results in order to achieve results that have a symmetric and approximate normal distribution. Uncertainty intervals are then calculated in the transformed space and back-transformed to the original space. The transformation includes a parameter, B, that needs to be optimized, and this can be based on real results, modelling of results, or on judgement. Two important reference points are B equal to 1 that corresponds to an approximate normal distribution of the original measurement results, and B approaching 0 that corresponds to an approximate log-normal distribution of the original measurement results. Comparisons are made with uncertainty intervals calculated using other expressions where it is assumed that measurement results have a normal distribution or a log-normal distribution. Implementation of the approach is demonstrated with several examples from chemical analysis.
Expression for uncertainty intervals handling skewness when the relative standard uncertainty is independent of the measurand level
Uncertainty intervals for many measurement results are typically reported as symmetric intervals around the measured value. However, at large standard uncertainties (> approx. 15 %–20 %), it is necessary to consider asymmetry of the uncertainty intervals. Here, an expression for calculating uncertainty intervals handling asymmetry when the relative standard uncertainty is independent of the measurand level is presented. The expression is based on implementation of a power transformation (\({x}^{B}\)) for transformation of measurement results in order to achieve results that have a symmetric and approximate normal distribution. Uncertainty intervals are then calculated in the transformed space and back-transformed to the original space. The transformation includes a parameter, B, that needs to be optimized, and this can be based on real results, modelling of results, or on judgement. Two important reference points are B equal to 1 that corresponds to an approximate normal distribution of the original measurement results, and B approaching 0 that corresponds to an approximate log-normal distribution of the original measurement results. Comparisons are made with uncertainty intervals calculated using other expressions where it is assumed that measurement results have a normal distribution or a log-normal distribution. Implementation of the approach is demonstrated with several examples from chemical analysis.
期刊介绍:
Accreditation and Quality Assurance has established itself as the leading information and discussion forum for all aspects relevant to quality, transparency and reliability of measurement results in chemical and biological sciences. The journal serves the information needs of researchers, practitioners and decision makers dealing with quality assurance and quality management, including the development and application of metrological principles and concepts such as traceability or measurement uncertainty in the following fields: environment, nutrition, consumer protection, geology, metallurgy, pharmacy, forensics, clinical chemistry and laboratory medicine, and microbiology.