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The class of SSP methods investigated here is defined by the property that the number of stages s is greater than the order k of the method.
All the methods investigated here are p-value based methods, the advantage of which is their flexibility and wide applicability.
The methods investigated here are likely to play an important role in identifying pathways that contribute to complex traits.
The methods investigated here, or simple modifications of these methods, could also be applied to other phenotypes, such as case-control status or time-to-event outcomes.
Of the methods investigated here, the Branson & Whitehead method gave the smallest bias and was seen to be robust in a variety of scenarios.
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The analytical method investigated here was the Laser-Induced Breakdown Spectroscopy (LIBS) combined with a Partial Least Squares (PLS) chemometric tool that can achieve the best model to predict fiber content in sugar cane.
Two methods are investigated here.
While our motivation has been to reduce the computation in dealing with binary traits, it is clear that the issues and methods that we investigated here can be applied more generally to other questions, for example for quantitative-trait linkage analysis (e.g. Amos 1994; Blangero et al. 2001), where both segregation and linkage is required.
The alert method we have investigated here could be a simple solution to address this.
The impact of the proposed filtering method has been investigated here in the context of differential analyses.
Internal symmetries for the Drucker Prager material model and numerical methods based on them are investigated here.
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