On Using Bootstrap Approach for Uncertainty Estimation
نویسنده
چکیده
Computer-Intensive methods for estimation assessment provide valuable information concerning the adequacy of applied probabilistic models. The bootstrap method is an extensive computational approach to uncertainty estimation based on resampling and statistical estimation. It is a powerful tool, especially when only a small data set is used to predict the behaviour of systems or processes. This paper provides a methodology to investigate uncertainty evaluation by bootstrap and a procedure to obtain confidence bands for linear and nonlinear models used in data analysis and design measurements. Also statistical considerations for proficiency testing are given. Numerical examples and graphical visualizations will be shown for a case study.
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