Time course of PEEP, albumin, PaO2/FIO2 ratio, EVLWI, daily and cumulative fluid balance, SOFA score, and IAP was described by clustered error bar graphs representing mean ± standard error.
As the initial condition for the temperature distribution in the model space, we use a layered thermal state of a cooling oceanic plate described by an error function.
Indeed, the ESF is obtained by the convolution of an edge profile with a Gaussian beam function, resulting in an image profile described by the error function, erf.
Time course of CLI, total SOFA score, EVLWI, APP, daily, and cumulative fluid balance was described by clustered error bar graphs representing mean ± SE.
The wind power forecasting errors described by the absolute error e i can be simulated by ARMA time series model.
Cointegration techniques provide an excellent framework to test for the existence of a stable long-run equilibrium relationship between the non-stationary series (Engle and Granger 1987).9 This relationship can be described by an error-correction version (ECM) of an autoregressive distributed lag approach (ARDL).
The within-subject variability (residual error model) for the observations is described by a proportional error model, and the proportional residual errors are assumed to be independent and normally distributed with zero means.
The residual error in the L-DOPA concentration was described by a proportional error model: C obs, ij = C pred, ij ⋅ (1 + ε ij ), and the residual error in the DOPAC or HVA concentration was described by an additive error model: C obs, ij = C pred, ij + ε ij where Cobs, ij represents the jth measured L-DOPA, DOPAC or HVA concentration for the ith individual predicted by the model.
The interindividual variability and residual error for both models were best described by a proportional error model.
The error in each estimate (parameter) used in the model, as described by its standard error, was characterized using an appropriate probability distribution (Table 1), except costs that were assumed known in the context of the evaluation.
