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Thanks to the intrinsic extensibility of RDF models, variants could be proposed to broaden its scope.
We proposed a more advanced forecasting system based on two components: a pattern identification procedure, using statistical tests, and a set of different forecasting models, variants of the exponential smoothing.
Within a number of these models, variants exist with different alleles, promoters, and genetic backgrounds.
Additionally, the differences in reliability of the training variants due to the different experimental designs were also considered for training of the VQSR models; variants from the experimental cross were assigned a higher prior likelihood of being correct than those from the association panels.
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In all regression models, variant rs7903146 proved significantly associated with angiographically characterized CAD in the total study population.
At last, the different model variants are compared with modeling approaches described in the literature.
The following model variants were considered.
For all model variants, parameters were fit to the accuracy and response time data (from both correct and error trials).
Six different models and two model variants participated in the inter-comparison and simulations were made for the period 2006 to 2009.
This study uses CCR and BCC models, which are DEA model variants, to calculate efficiency indexes.
The component models differ depending on the geographic region represented by regionally specific model variants.
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