2007
DOI: 10.2172/910198
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Experimental uncertainty estimation and statistics for data having interval uncertainty.

Abstract: This report addresses the characterization of measurements that include epistemic uncertainties in the form of intervals. It reviews the application of basic descriptive statistics to data sets which contain intervals rather than exclusively point estimates. It describes algorithms to compute various means, the median and other percentiles, variance, interquartile range, moments, confidence limits, and other important statistics and summarizes the computability of these statistics as a function of sample size … Show more

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Cited by 144 publications
(38 citation statements)
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References 155 publications
(125 reference statements)
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“…Similarly, MacEachren et al [42] define the following seven goals for uncertainty visualization: Actual application of such general rules, especially for the case of probabilistic representation of uncertainty, is illustrated, for example, in [26,28,46] or in the overview papers [15,47]. In addition to that, if mixed interval-probabilistic techniques are used to represent uncertainty, tools and theories described in, for example, [21,48] can be used for visualization. In particular, the use of Demspter-Shafer or p-box theories described therein allows one to work with and visualize uncertain distributions by defining belief and plausibility functions (or lower and upper bounds on probability).…”
Section: Visualizing Uncertaintymentioning
confidence: 99%
“…Similarly, MacEachren et al [42] define the following seven goals for uncertainty visualization: Actual application of such general rules, especially for the case of probabilistic representation of uncertainty, is illustrated, for example, in [26,28,46] or in the overview papers [15,47]. In addition to that, if mixed interval-probabilistic techniques are used to represent uncertainty, tools and theories described in, for example, [21,48] can be used for visualization. In particular, the use of Demspter-Shafer or p-box theories described therein allows one to work with and visualize uncertain distributions by defining belief and plausibility functions (or lower and upper bounds on probability).…”
Section: Visualizing Uncertaintymentioning
confidence: 99%
“…On the contrary where there are big differences and results it could be necessary to reconsider the analysis [14]. In this sense we want to investigate about Y c u in order to assess the differences related to the different assumptions k. The idea is that we build interval data [10] by considering the different assumptions k in order to test the robustness of the results [14]. This idea is based on the concept of sensitivity analysis [20].…”
Section: Checking Robustness Of the Composite Indicatorsmentioning
confidence: 99%
“…The motivations to use intervals and interval data in the analysis of the composite indicators can be the close relationship between intervals and sensitivity analysis [20]. In particular in this sense interval statistics can be used in the case the data can have a specific point estimate and can be characterized by relevant and reliable ranges [10]. So we start from the use of the different assumptions on k and we apply different variations to obtain a different value for the same composite indicator Y c,k u .…”
Section: Using Interval Composite Indicatorsmentioning
confidence: 99%
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