Sentence examples for a robust statistic from inspiring English sources

In our work, we use the mean statistic with the awareness that it is not a robust statistic.

The proposed procedure is based on intrinsic time-scale decomposition (ITD) and contains (i) an improved iteration termination condition for ITD with on-line back-redecomposition and (ii) a novel hypothesis test with a robust statistic of variation coefficient which enables online monitoring of time-variant oscillations.

We selected median as a more robust statistic than mean (here, we use the term robust in the statistical sense, denoting that median provides a better characterization of the set of values in the presence of outliers and not that it provides any robustness against adversarial evasion).

We follow Majumder et al. (2012) and estimate equation (4) by using medians instead of means which is a more robust statistic with respect to outliers.

ORT was proposed as a more robust statistic that utilizes the absolute difference of each expression value from the median instead of the squared difference of each expression value from the average.

ORT has been proposed as a more robust statistic that utilizes the absolute difference of each expression value from the median instead of the squared difference of each expression value from the average.

Furthermore, in situations where changing the background subtraction or normalization technique is not possible because the original data is not available, using a more robust statistic like Z(Imin) will be advantageous.

A robust diversity statistic would have to measure whether traditionally under-represented groups have an equal footing in all departments and all job descriptions, what ecologists call β-diversity [1], [2].

The optimization of the robust statistic function is iterative and usually requires complex parameter adjustments.

Even though robust statistic provides the tolerance against impulsive noise, its optimization problem is often difficult.

Even though robust statistic methods have been developed for evaluation of multiple reference genes [7], [8]; the selection of particular genes or the number of reference genes remains unchanged in different experiments.