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Association of independent predictors with need for organ dysfunction support was studied through logistic regression models.
A small group (27%, n=40), were considered bad predictors with ≤25% accuracy.
Finally, Univariate and multivariate analyses were employed to assess the association of predictors with consciousness recovery.
Stepwise backward elimination was performed to identify independent factors, with retention of predictors with p < 0.2.
Reference [3] investigated the admissibility of linear predictors with inequality constraints under the quadratic loss function.
The model performance of the SGT could be further improved by adopting predictors with greater spatial resolutions.
This simplifies information needs by landscape managers as it replaces two predictors with one.
Within each region a user specified predictive model associates the predictors with the response.
The 17 predictor dataset was tested using a pairwise Spearman correlation analysis, and all predictors with Spearman correlation ≤ 0.7 were selected, retaining those predictors with the greatest ecological relevance for the species.
Agarwal et al. [18] present a system for learning linear predictors with convex losses on a cluster of 1000 machines.
Calibration and validation procedures are described and illustrated for the predictors with the aid of published experimental data.
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