Sentence examples for owing to regression from inspiring English sources

However, owing to regression toward the mean, using a clinical outcome to identify subjects for a study and a related clinical outcome to follow those same subjects over time is not ideal.

A further limitation was that we used a single measurement of HbA1c at baseline, which might have underestimated the relationship owing to regression dilution bias (38), and changes in HbA1c during the 15-year follow-up period were not taken into account.

32 Such misclassification would result in an overestimate of the reoperation rate among women with only invasive disease, and the relative risk between the risk of reoperation and the presence of carcinoma in situ would be underestimated owing to "regression dilution".

The F (Fishers' variance ratio) value is calculated by taking the ratio of the mean square owing to regression and the mean square owing to error.

Analogous to the simulation study, the prediction error estimates increased with larger test sets and thus smaller training sets owing to deteriorated regression vector estimates.

The important point is that the calculation of SLT using LAN will not vary significantly owing to the small nodal regression rate, which is less than 0.5° in our case or around 2 min/day assuming that 1 solar hour equals 15°.

In order to assess the association between photographed children's characteristics and participants' ability to correctly identify the weight status of children (Table 2), the generalized estimating equation (GEE) approach [ 21] was used with logistic regression owing to correlated outcome data (since each participant rated all of the 12 unrelated children's weight status).

Treatment group assignment can, however, have a different risk pattern at baseline, and this can lead to groups being less comparable after statistical adjustment (for example, regression) owing to the "constant risk fallacy" where the assumption of constant risk across different organisations (for example, hospitals) may be inappropriate.

A logistic regression model appeared to be better than a linear regression model owing to non-normally distributed residuals.

This method was first introduced as a robust tool by Vapnik (2000) and has found drastic applicability in the field of regression estimation owing to its many attractive features and higher generalization ability in comparison to traditional ANNs.

As having the closed form solution, the least squares support vector machine (LSSVM) has been widely used for classification and regression problems owing to its competitive performance compared with other types of SVMs.