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The accuracy profile based on tolerance intervals was used to generate a complete validation report.
This paper proposes an approach based on tolerance intervals to address uncertainty for RAMS+C informed optimization of design and maintenance of safety-related systems using a combined Monte Carlo (MC) (simulation) and Genetic Algorithm (search) procedure.
Finally, an approach based on tolerance intervals and accuracy profiles was applied in order to thoroughly validate the method in a range of concentrations comprised from 3 to 15 µg mL−1 using normalized band intensities.
Our proposal for inference about the TDI estimate is based on tolerance intervals (TI), provided that we estimate the TDI by deriving the limits of a probability interval that contains a specified p-proportion of the resulting estimated normal distribution.
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Huang et al. ([2008]) developed a fuzzy interval semi-infinite programming model for waste management, which can allow for the existence of tolerance intervals for each of the constraints and address the possible effects of energy prices on the identified waste management policies.
An addition advantage of tolerance intervals is simplicity of interpretation.
The selected working condition was then fully validated using accuracy profiles based on statistical tolerance intervals in order to evaluate the reliability of the results generated by this LC/ESI-MS stability-indicating method.
Secondly, these methods were fully validated for a concentration range of 70 130% of the target active content using the accuracy profile approach based on β-expectation tolerance intervals.
The method was validated following an approach using accuracy profiles based on β-expectation tolerance intervals for the total error measurement, and assessing the measurements uncertainty.
The LOD and LOQ were computed based on prediction and tolerance intervals using WLS to accommodate nonconstant variance of BSI, that is, significant increases of the variance of BSI as IPC increased.
In this paper, we construct tolerance intervals based on the expensive measurements that are obtained using randomized nomination sampling (RNS) with the help of inexpensive auxiliary information.
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