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In practice, we can calculate a p-value by replacing μ j with z j and σ j 2 with Var z j) where Var z j = ∑ k = 1 p Var x j k β ^ k = ∑ k = 1 p x j k 2 s β k 2, is derived based on approximation and s β k 2 is the variance estimation of β ^ k by using estimated coefficient as weight or Var z j = ∑ k = 1 p Var x j k ŵ k sign β ^ k = 3 ∑ k = 1 p x j k 2 by using Wald statistics as weight.
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In addition, most of these methods are based on approximations, and make simple assumptions about the study design.
For sufficiently-low accuracy levels, output-based adaptation is shown to be advantageous in terms of degrees of freedom when compared to uniform refinement and to adaptive indicators based on approximation error and the unweighted residual.
Then two thresholds, which are based on approximation information and tolerance information, are integrated to evaluate collisions between polygonal models.
In this study, a fast and reliable method for 3D reconstruction is proposed based on approximation of correlation functions and phase recovery algorithm using one and two perpendicular cut sections.
When β is sufficiently large, an analytical approach, based on approximation at infinity of both exact and approximate nth moments, is used to prove equality between them.
The mean of the bed measurements was divided into three categories (<10 V m−1, 10 <20 V m−1, ⩾20 V m−1) based on approximations of the median and 90th percentile of the control population.
Although the analytical results are not exact and are based on approximations that provide theoretical tractability, they are for the most part consistent with the numerical simulations.
The proposed forest plot discloses and summarises the essentials of a given network-based treatment comparison: the weight given to each path, the consistency between all paths, the comparison between the estimate based on the approximation and on the whole network, and the residual evidence that is not included in the approximation is condensed into a pseudo estimate.
We solve it by a branch-and-bound algorithm based on Outer Approximation and a heuristic algorithm exploiting the decomposition and reciprocal update of two submodels.
Further, in order to improve the robust stability and filtering ability to tolerate more parameter fluctuations, process delays and to attenuate much environmental molecular noises, based on fuzzy approximation and LMI technique, a systematic design method is proposed for nonlinear stochastic time-delayed gene networks.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com