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How to characterize the cross-site variability of the model bias factor is important for design of pile foundations based on site-specific load test data.
How to update the statistics of the model bias factor, when applied to a future site, with site-specific load test data is also described.
It is found that, given a certain number of site-specific pile load tests, the effect of updating depends on the mean and the COV of the measured model bias factor.
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We show in Bias Factor Expression β in Heuristic Modeling that the bias factor β is equivalent to a length along the B0 or B∞ axis.
In the Cox regression model, bias caused by demographic factors, reasons for revision, and number of previous operations could be partly compensated for.
Users of a model must verify that a bias factor between 0.75 and 1.25 has been obtained in product validation studies before predictions are applied for assessment or management of seafood safety.
A comparison of the calculated bias factor with a model based on elasticity theory reveals around 30% discrepancy under conditions representative for electron irradiation at 600 °C.
The cross-site variability (i.e., variability from site to site) makes the statistics of the bias factor of a design model vary from site to site.
Do Bayes factors help to mitigate the effect of model bias?
The models also have been shown to have low bias and high precision: The normalized mean bias factor for PM2.5 is –1.6%, and the absolute value of the prediction errors is 1.61.
In addition, the bias factor (Eqn. 14.5) and accuracy factor (Eqn. 14.6) are most important indices of performance for predictive models (Ross, 1996).
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