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The model estimates have typically been validated using experimental data from bio-monitoring.
Secondly, the ITS model estimates have not been controlled for covariates.
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Therefore, multipollutant model estimates had larger standard errors and smaller posterior probabilities of being greater than zero than their single-pollutant counterparts.
The bigger problem though was that, until now, modelling estimates have been based on a discredited technique, commonly referred to as "input-output analysis".
Moreover, the impact of possible measurement variability on model estimate has been performed by using Monte Carlo simulation using normal distribution (Baughman et al. 2010).
Model-derived estimates have been proposed for quinine in uncomplicated falciparum malaria (42).
As a result, some basic definitions, datasets, statistical models and estimates have been revised after original publications [2,5,9,11-13].
For instance, the original BRL model diffuse fraction estimates have MeAPE errors ranging from 16%to51%1%, while the adjusted BRL model gives errors from 9% to 26%.
After selection of an appropriate process model structure, parameter estimates have to be obtained based on real-life experiments.
After selecting an appropriate process model structure, parameter estimates have to be obtained based on real-life experiments.
Second, though its assumptions are weaker than parametric models, model estimates still have good statistical properties, and the efficiency of statistical tests does not suffer greatly.
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