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The use of kernel densities and confidence intervals to cope with insufficient data in validation experiments

机译:使用核密度和置信区间来应对验证实验中的数据不足

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摘要

A novel procedure to establish probability density distributions based on insufficient data is introduced. The approach requires the selection of a confidence level to cover the unknown distribution. The use of kernel densities is proposed for its high fidelity to data and its capability to represent correlations correctly. It is demonstrated that the proposed approach can be applied successfully for validation, leading to measures for the validity in the validation and accreditation experiments and provides consistent predictions based on the required confidence level and the size of available data points of the calibration data.
机译:介绍了一种基于不足数据建立概率密度分布的新方法。该方法要求选择置信度以覆盖未知分布。提出使用核密度是因为其对数据的高保真度及其正确表示相关性的能力。结果表明,所提出的方法可以成功地用于验证,从而得出验证和认可实验中的有效性度量,并根据所需的置信度和校准数据的可用数据点大小提供一致的预测。

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