Sentence examples for assumptions regarding data from inspiring English sources

We use these statements to examine fundamental assumptions regarding data sharing using data federations and grid computing.

Rank-order correlations, shown in the right-most column, are independent of all assumptions regarding data distribution or transformation, and are also conservative in that they are influenced equally by strains of moderate or extreme longevity.

The Random Forest classification algorithm is extensively used in ~omics data analysis because it is less resource-intensive than many classification algorithms (e.g.: Bayesian algorithms), it makes no implicit assumption regarding data properties and it is specifically suited to deal with the small n large p problem due to the use of many weak classifiers (see below).

The CID does not require distributional and functional assumptions regarding the data and is useful for analyzing noisy microarray data.

Different cost functions correspond to different distributional assumptions regarding the data, the appropriate choice depends on whether a forecast of absolute pollutant concentration or prediction of exceedence events is of principle importance.

At the same time, a strategy may offer a reasonable action before the arrival of such a query, employing some rational assumptions regarding the data distribution.

There are three major benefits to the use of entropy (see [26] for a more in depth discussion), because entropy: 1) does not make assumptions regarding the data distribution (e.g., normality, random variance); 2) can be made conditional upon the satisfaction of certain criteria; and 3) can be linked theoretically to concepts of information processing and uncertainty [3], [4], [27].

However, statistical significance was reached for only one outcome measure and was not robust for different assumptions regarding missing data.

Nonetheless, such algorithms are constructed based on specific assumptions regarding the data, such as the causal sufficiency assumption (for more details, see [ 12, 14]).

Methods such as MMRM, which require less restrictive assumptions regarding missing data, may now be easily implemented with standard software [ 4, 5, 18, 19].

Sensitivity analyses were performed in 35% of the trials, but most (63%) did not weaken the assumptions regarding missing data from their primary analysis.