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Moreover, a numerical approach with error estimation to solve general integro-differential-difference equations using Dickson polynomials has been developed in [12].
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We show that the locations of priority areas change between the standard and probabilistic approaches, with errors of omission and commission likely to occur if reserve design does not accommodate mapping accuracy.
The location of this event was simultaneously acquired using a stepwise refined grid search approach, with an error of ~2.2 km.
In place of this, we used a generalized estimating equation modified Poisson regression approach with robust error variance to produce direct assessments of the relative risks [27].
For dichotomous dependent variables, we used a modified Poisson regression approach with robust error variance (SAS PROC GENMOD) to assess relative risks (RRs) with 95% confidence intervals (CIs) (Zhou 2004).
Since data are from aggregated cell populations, the imaged protein data are very smooth and successive data points of the Q-PCR and in vitro time series come from different samples of cell populations, we choose to fit the model using the mean ODE approach with independent error.
A sensitivity analysis has also been carried out for the proposed approach with perturbation errors in the rotor and bearing models, to confirm the robustness of the method.
The experimental results demonstrate the accuracy of the proposed approach, with average errors of 0.07 for both the degree of anisotropy and tensor norms.
We have extended Multiple Linear Regression classification with our new approach of Estimating with Error Prediction.
The third one is our new approach of "estimation with error prediction" for multiple-linear regression.
The LOD scores that were estimated using the quantitative approach have the greatest range, followed by those estimated using the perturbed approach with an estimated error, followed by those estimated using the perturbed approach with an assumed error.
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