Sentence examples for data are contaminated with from inspiring English sources

Numerical examples show that results of this SVR-based approach for large-scale structural health monitoring are accurate and robust, even when observed data are contaminated with different kinds and intensity levels of noise.

Specifically, data contamination may occur due to several reasons such as the measurement errors, departure from the true model, etc. Accordingly, it becomes necessary to investigate the performance of the estimators under a scenario in which data are contaminated with outliers.

The same is true if the data are contaminated with error which needs to be taken into account when producing Aleast-squares [ 20].

Our comparative evaluation of the performance, stability, and robustness of the seven methods for estimating the accuracy of genomic prediction when phenotypic data are contaminated with outliers compared with when they are not (the benchmark) yielded many interesting and useful insights.

The capabilities of the proposed methodology are illustrated in problems from nonlinear solid mechanics with special attention to cases where the data is contaminated with random noise and the scale of variability of the unknown field is smaller than the scale of the grid where observations are collected.

Additionally, simulated data were contaminated with eye blink artefacts and subsequently corrected to assess the residual activity remaining following correction.

A simulation in R using known regression coefficients shows the technique accurately estimates the true regression coefficients when the data are contaminated by outliers, with performance comparable to that of Aeberhard et al. (2014).

The approach is based on the important assumption that the data are contaminated (in particular with high values) that is not appropriate in the case of healthcare resource use and costs where zero or high observations are true values.

The evaluation is unbiased in the following sense: cross-validation on retrospective data is likely to yield overoptimistic performance estimate, as some of the data sources are contaminated with knowledge from gene disease associations.

Fortunately, several standard statistical software packages have allowed practitioners to use robust regression estimators to easily fit data sets that are contaminated with outliers.

On the one hand, most real world optimization problems are contaminated with uncertain data, especially traffic optimization problems, scheduling problems, portfolio optimization, network flow and network design problems.