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Conditional on the interaction with deprivation, the association between absolute change in NHS allocation and change in mortality amenable to healthcare was approximately linear (test for non-linearity: P=0.16, see regression diagnostic in supplementary appendix 3).
A linearization about the stationary solution (X t)equiv1) leads to the linear test problem (4.1).
The proposed method, when applied to a linear test equation, is shown to be unconditionally stable.
This will define an easy-to-apply linear test for exact linear aggregation.
In Section 4, the mean-square stability of the CSSθ method for linear test equation is studied.
In this section, we focus on the MS-stability of the IM method applied to a scalar linear test equation.
A linear test set consisting of 6 mixtures was used to validate the prediction ability of the suggested models.
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Additionally, this paper proposes a non-linear test method for the quality assessment of the required non-predictability property, which relies on feedforward neural networks.
Here we describe the method in detail and carefully analyze its performance using several 2D linear and non-linear test cases.
First, we focus the analysis on one- and two-dimensional scalar non-linear test cases to examine the accuracy of the approach using a finite difference discretisation.
The robustness of the method is demonstrated for non-linear test cases, in particular on an equilibrium solution with a pressure pedestal.
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