Sentence examples for extreme underestimation from inspiring English sources

As a result, SW ℓ2,1-SVD becomes ℓ1-SVD in the extreme underestimation case.

It is illustrated in Figure 8, in the extreme underestimation case, i.e., ANS = 0, the result of SW ℓ2,1-SVD and ℓ1-SVD is identical, which corroborates ℓ1-SVD is a special example of SW ℓ2,1-SVD.

Considering two cases, i.e., the estimate of the number of sources P ^ = 0 (the extreme underestimation case) and P < P ^ ≤ M - 1 (the overestimation case), where the real value of the number of sources is assumed as 0 < P < M − 1.

A second factor added by Jibson and Harp (2012) explaining the extreme underestimation of affected surface area and epicentral distance by existing laws is the fact that those ones (including the one of Keefer and Wilson 1989) were partly based on older inventories mainly compiled from smaller areas near the epicentre.

This may be an extreme underestimation.

These large differences highlight the extreme underestimation of risk based on pathogen-specific diagnostic codes.

The expected value of the prescription cost was given by: <img src="http://journals.plos.org/plosone/article/asset?id=info?doi/10.1371/journal.pone.0000592.e005.PNG" class= inline-graphic"/> Few extreme underestimations dominate the variance of the prediction error [Y−E(Y|X)]: the maximum error was circa €27,912, that was almost 20 times larger than the absolute value of the minimum error (€1,483).

However, for group estimation, there is no extreme over- or underestimation in the distribution of BF%.

The Bland-Altman plots indicated no extreme over- or underestimation in the distribution of BF% using the foot-to-foot BIA for both adults and children by gender, BMI, and ethnicity groups.

It results in an MP scattered power overestimation for "typical" LMS incidences and in underestimation for "extreme" LMS incidences.

Through examples with actual data, we show how such loss of information in prewhitened time series of tree rings (so-called "residual chronologies") can lead to the underestimation of extreme conditions in climate and hydrology, particularly droughts, reconstructed for centuries preceding the historical period.