Model Validation and Uncertainty Quantification, Volume 3

2 The Need for Credibility Guidance for Analyses Quantifying Margin and Uncertainty 23 19. Hemez, F., Atamturktur, H.S., Unal, C.: Defining predictive maturity for validated numerical simulations. Comput. Struct. 88(7–8), 497–505 (2010) 20. Pearl, J., Glymour, M., Jewell, N.P.: Causal Inference in Statistics: A Primer. Wiley, Hoboken (2016) 21. Bareinboim, E., Pearl, J.: Causal inference and the data-fusion problem. Proc. Natl. Acad. Sci. 113(27), 7345–7352 (2016) 22. Oberkampf, W., Barone, M.: Measures of agreement between computation and experiment: validation metrics. J. Comput. Phys. 217(1), 5–36 (2006) 23. Liu, Y., Arendt, P., Huang, H.: Toward a better understanding of model validation metrics. J. Mech. Des. 133(7), 071005 (2011) 24. Stone, M.: Cross-validatory choice and assessment of statistical predictions. J. R. Stat. Soc. Ser. B Methodol. 36, 111–147 (1974) 25. Efron, B.: Estimating the error rate of a prediction rule: improvement on cross-validation. J. Am. Stat. Assoc. 78(382), 316–331 (1983) 26. Hastie, T., Tibshirani, R., Friedman, J.: The Elements of Statistical Learning: Data Mining, Inference, and Prediction, 2nd edn. Springer Series in Statistics. Springer, New York (2009) 27. Zeng, Z., Di Maio, F., Zio, E., Kang, R.: A hierarchical decision-making framework for the assessment of the prediction capability of prognostic methods. Proc. Inst. Mech. Eng. O: J. Risk Reliab. 231(1), 36–52 (2017) 28. EricksonKirk, M., et al.: Sensitivity studies of the probabilistic fracture mechanics model used in FAVOR version 03.1. In: NUREG-1808, US Nuclear Regulatory Commission, ADAMS ML, vol. 61580349 (2004) 29. Coles, S., Bawa, J., Trenner, L., Dorazio, P.: An Introduction to Statistical Modeling of Extreme Values, vol. 208. Springer, London (2001)

RkJQdWJsaXNoZXIy MTMzNzEzMQ==