Sentence examples for recognized regression from inspiring English sources

In the late 19th century, William B. Coley, with the assistance of established anecdotal theories of the beneficial effect of fever on tumors (McCarthy 2006), recognized regression and, in some cases, necrosis of tumors in advanced cancer patients suffering concomitant bacterial infections.

I recall sharing a dinner table in the late nineties with Federal Circuit Judge Frank Easterbrook (an adherent of the Chicago School of Economics), who sneeringly described Derek Bok of Harvard as someone who wouldn't recognize a regression analysis if it hit him between the eyes.

It is widely recognized that stepwise regression may not give the best model if there is a high levels of correlation between independent variables.

However, residual spatial variation was recognized in all regression models, suggesting that further investigation of spatial information not considered in this study may improve these models.

This fact still holds between classification and regression because regression could be recognized as a kind of infinite labels prediction task with lower accuracy comparing with classification under the same situation.

Further, the delay of events is a descriptive measure that conceptually matches the increasingly recognized AFT and quantile regression modeling techniques.

Most of them were in use of ACE-inhibitors and calcium antagonists, which are recognized efficients at LVH regression in hypertensive patients [ 39].

This strategy for logistic regression recognizes an entire cohort as controls.

In contrast, quantile regression recognizes the data truly cannot exceed the upper boundary, as opposed to assuming censoring at the boundary.

When adjusted for recognized risk factors using logistic regression analysis, infection of amniotic fluid (OR=6.6) and genito-urinary tract infection (OR=1.64) remained the independent risk factors associated with PPROM.

It has been widely recognized that Poisson models outperform the standard regression models in handling the nonnegative, random and discrete features of crash counts [10, 11].