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The model is also meritorious in statistics because the precision of parameter estimation and power of QTLdetection can be increased by modeling the mean-covariance structure with a small set of parameters.
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In common with gene expression studies and expression quantitative loci (eQTL) mapping, it may be anticipated that statistical power will be increased by modelling unknown as well as known factors in association studies (8).
Although glucose accumulation could be increased by model adjustments, these additional complexities are inessential to demonstrate the central hypothesis of this work and have not been implemented.
The accuracy of design-based sampling strategies can be increased by using regression models at the estimation stage.
This shows that type one error rates are similar, that power is similar for two SNP models, but that power can be increased by averaging over three SNP models compared to two SNP models, particularly when there are really three or more distinct causal variants (Fig. 4).
This paper proves that the completeness of this analysis may be increased by applying the qualitative model of the process in the form of a graph of a process.
It can be seen that the effective horizon could be increased by applying more complicated nonlinear models and considering the teleconnection for input variables as well.
It has been shown that the prediction performance of an LN model can be increased by using a nonlinear transformation of the stimulus (e.g., a transformation may capture nonlinearities at earlier stages of processing) that precedes the linear filtering stage [25], [47], [48].
Accuracy of the selection index (that is, accuracy of predicted breeding values in the animal model) can be increased by adding more records from relatives in the analysis.
Results from this study indicate that the accuracy of the GS models can be increased by using markers identified from linkage and association studies.
The computational complexity of this relatively simple model can be increased by increasing the number of plasmids per reaction network from 1 to 10 and the number of substrate molecules by a factor of 10 (denoted the geneA_CFTT_0p0 – 10× model).
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