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This research designs a method to correct for sample selection in a sample of recent movers.
However, it is not widely appreciated that the data analysis for these studies has to take into account the non-random nature of sample selection in these designs.
We feel that this inconsistency may arise either due to the influence of the large latitudinal extent of sample selection in the previous studies resulting in dissimilar seasonal cycles across all the sampling locations, loose temporal constraints on core-tops and/or possibly from limited sample numbers, few specimens analyzed per sample, and a non-rigorous treatment of uncertainty.
There is no evidence of sample selection in the mobility equation11.
Significant limitations regarding the design, sample selection (in Germany) and few control variables must be noted.
This is similar to the sample selection in Kronick [10], who compares the uninsured to individuals with employer-provided health insurance.
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Unlike the previous two mentioned reports [ 7, 8], extremely care was taken in sample selection, resulting in homogenous anthropometric parameters such as age, body mass index (BMI) and gender between groups.
Let denote the selected samples, where for the sample selection scheme in [27].
However, when interpreting these data the different sample selection methods in Belgium and Australia should be kept in mind.
Details of sample selection are given in below.
Furthermore, the training sample selection scheme in the original LDA-style boosting scheme proposed in [27] tends to prevent the inclusion of "difficult" (hard to classify correctly) samples in subsequent boosting steps.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com