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In contrast, standard residual-based bootstrap methods for models with i.i.d.i.d
How to design effective error-control methods, for models with a large steepness parameter β, is, as far as the authors know, still an open problem.
Solution methods for models constituted by dual and triple combinations of c j, a kj and b k coefficients and also for the case that c j 's are random variable are different.
Notably, GEMOT attains similar performance to that of the compared methods for models M1 M3, but outperforms the existing methods when adding synthetic M4 and M5 samples.
Our simulation study (see 'Materials and methods') indicated that GEMOT attains similar performance to that of the compared methods for models M1 M3, but outperforms the existing methods when adding the remaining models.
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We analysed cross-validation methods for model assessment.
The chapter then deals with methods for modeling chemical kinetics.
There are many methods for modeling population viability.
This includes methods for model quality control, data and model integration, model comparison and model identification.
We might need to design more sophisticated methods for model checking and model selection.
Having the Pareto nearest neighbourhood defined, we present two methods for model identification.
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