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Nonetheless, modeling such data poses new challenges related to data volume, diversity, inhomogeneity and the required granularity level.
For modeling such data, we investigate the use of weighted congestion indices based on various aggregation and spread functions.
Also, SNP data are ordinal (0, 1, or 2 minor alleles for each SNP) and different ways of modeling such data can be considered (e.g., dominant, recessive genetic models).
Cox semi-parametric method [ 12] has also been extensively used for modeling such data.
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However, these distributions are often asymmetric and bimodal; to date, there is no joint distribution to model such data.
Several regression techniques have been proposed to model such data in cross-sectional studies, however, methods applicable in longitudinal research are less well researched.
Eventually, these empirical risk estimates could be calculated using clinical and administrative data by systems capable of modelling such data in real time, providing earlier warnings about new drugs associated with increased risks of fall injuries.
While the marginal distributions of shared orthologs are often found to be asymmetric and bimodal, to date there is no joint distribution with closed-formed marginals to model such data.
The aim of this paper is to (a) summarise the issues that need to be considered when modelling recurrent sports injury data where the time before/between injuries is of interest and (b) to assess and compare the suitability of the CoxPH model and its extensions for modelling such data.
In addition to measured data for validation of predictive models, such data can be used by designers to characterize and improve the energy efficiency of engineering devices.
When combined with homology modelling, such data can be used to produce atomic-resolution models [15].
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