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Poisson regression was chosen as the most appropriate method of analysis for the mortality data because of the size of the data set and the ability this method provides to analyse data files in collapsed (aggregated) form.
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Thus, this method provides us to understand the biological functions of miRNAs.
First, it has the potential to greatly improve the practice of HLA typing/matching for clinical transplantation procedures [ 13, 15, 20, 37], as this method provides access to alleles that are otherwise un-typed using current methods.
When coupled with uniform estimates, this method provides a strategy to extend known Riemannian results to the subRiemannian setting.
This method provides a mean to extract important samples from the dataset and to eliminate the unimportant ones.
This method provides the ability to pick up and transport particles or biological entities, leading to their isolation in complex samples (e.g. serum, urine, saliva).
This method provides another threshold to differentiate between wireless and congestion losses.
This method provides an opportunity to consider uncertainties in various economic parameters.
This method provides a way to controllably grow parallel and vertical FLG arrays.
This method provides a solution to ensure functionality of oxidoreductase enzymes in oxygen-free environments.
Focussing on median regression in particular, this method provides an approach to Bayesian robust regression for overdispersed count data.
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