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Variable-stepsize explicit peer methods were designed and studied by Weiner et al. in 2008, 2009.
This paper elaborates a global error estimation and control mechanism in explicit two-step peer methods.
Applied on two cancer types (ovarian and glioblastoma multiforme), our method achieved better prediction power (p-value: 6.18E−03 5.15E−11) than peer methods (GE.CNAs and GE.CNAs. Lasso).
kDDN consistently outperforms all peer methods under all scenarios.
Here we compare the eight peer methods along several performance fronts.
We used one-sided t-tests to assess whether kDDN performs better than the peer methods across all network sizes.
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The quality of matching between the two methods on other key variables did not differ except for education, with the peer method providing a better match for the survivors than the listed sample.
Numerical experiments and a comparison to other IMEX-Peer methods are included.
To construct super-convergent IMEX-Peer methods with favourable stability properties, we derive necessary and sufficient conditions on the coefficient matrices and apply an extrapolation approach based on already computed stage values.
Optimised super-convergent IMEX-Peer methods of order s+1 for s= 2,3,4 stages are given as result of a search algorithm carefully designed to balance the size of the stability regions and the extrapolation errors.
Optimised IMEX-Peer methods of order p= 2,3,4, are given as result of a search algorithm carefully designed to balance the size of the stability regions and the extrapolation errors.
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