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We propose a new class of matching conditions between the atomistic and the continuum regions.
Coarsened exact matching is a member of the Monotonic Imbalance Bounding (MIB) class of matching methods (further described in (Iacus et al. 2008)).
"Within the class of matching estimators, using only a single match leads to the most credible inference with the least bias, at most sacrificing some precision" (Imbens, 2004, p. 14).
Imbens suggested that "within the class of matching estimators, using only a single match leads to the most credible inference with the least bias, at most sacrificing some precision" (20, p. 14).
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Secondly, owing to the sample size, boundaries for classes of matching criteria were relatively wide.
In this study, the following balancing methods were used: Random undersampling This method randomly eliminates majority class cases with the aim of matching the quantity of minority class cases.
The degree of matching for class II improved with time as the numbers within the bank built up, but this effect was not apparent for class I.
The design strategy is very simple to apply and is demonstrated for three examples with different class of disturbances (matched and unmatched) to show the effectiveness of the design.
We therefore examine each sub-topic as a 3, 4, or 5-class problem, with the number of classes matching the range of possible scores for each particular sub-topic.
Among women with low semen exposure, effects of Class I matching were amplified (HLA-A matching, OR = 6.27, 95%CI = 1.04, 37.97; Class I matching, OR = 4.49 per one-match increase, 95%CI = 1.89, 14.50).
However, it is the server that returns fixed length lists of class names matching the introduced pattern for auto-suggestion.
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