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To recap, Problem 1 means that parametric CIs for the LD method will tend to be slightly too narrow, with the effect being more pronounced for large numbers of loci.
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Although violations of the assumptions of parametric statistical procedures suggested that non-parametric analysis would, in this instance, be a more appropriate indicator of treatment outcome, a lack of non-parametric alternatives to the ANOVA meant that parametric options were necessary.
This would mean that walls and doors had parametric properties and for example a wall would use the properties of the rest of the building such as the floor and ceiling heights to work out where it should start and end in the vertical direction.
This means that ellipsoidal parametric uncertainty resulting from model identification using this input spectrum will be shaped such that the largest possible polytopic uncertainty in controller parameters can be gained.
This means that the parametric "speed", (dot{s}(t)), of the curve can be described by a polynomial, making it possible to compute the length of the path, (s), as shown in Eq. (8): begin{aligned} s=int limits _{t_{i}}^{t_{f}}sqrt{dot{x}(t)^2+dot{y}(t)^2+dot{z}(t)^2},mathrm{d}t=int limits _0 ^1 |h(t)|,mathrm{d}t.
This means that a parametric modeling approach for epistasis requires much larger sample sizes than for tests of the effects of single loci.
This means that the non-parametric model component capture the data distribution in fine details.
In this optimized dataflow graph, production and consumption rates are of a more predictable parametric synchronous dataflow (parametric SDF) form, which means that for a given set of graph parameters, the dataflow rates are all constant.
However, the larger freedom regarding possible distribution functions explored by non-parametric methods means that datasets need to be very large to obtain accurate results with this method.
The other two methods listed in Figure 3, the Kendall tau rank correlation coefficient (tau) [ 97] and Spearman's rank correlation coefficient (rho) [ 98] are both non-parametric, which means that they do not rely on a specific distribution in the data.
This means that the benefits of performing large parametric design studies are often overshadowed by the time taken to complete them.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com