Sentence examples for model in populations from inspiring English sources

The Lexis ratio appears to be the first statistic suggested for testing for the presence of over- or under-dispersion relative to a binomial hypothesized model in populations structured in clusters.

Notably, the association was significant under a recessive model in Asian populations but under a dominant model in populations of European descent.

This approach consists of implementing a Bayesian random effects model in populations of 25 000 or less harnessing the improved precision obtained through intelligent Bayesian smoothing.

We propose a hybrid approach for the calculation of life expectancy using the Bayesian random effects model in populations of 25 000 or lower permitting the precise derivation of life expectancy in small populations.

Our finding that the simple Quebec Task Force Classification was not useful for prediction of outcome in a very severely affected group of neck pain patients, suggests a need to also test this more comprehensive Shellingerhout model in populations similar to ours.

We explored the methods for estimating life expectancy in small and large populations and propose a hybrid approach for estimating life expectancy using adjusted Chiang II in populations larger than 25 000 and a Bayesian random effects model in populations of 25 000 or less for intended use by health administrators to support planning and policy.

BVPs with indefinite weight arise from a selection-migration model in population genetics, see Fleming [1].

This work is devoted to the study of travelling wave solutions for some size structured model in population dynamics.

The Beverton-Holt difference equation when γ = δ = C = 0 : x n + 1 = β x n − 1 B x n − 1 + D, n = 0, 1, …, which represents the basic discrete model in population dynamics, see [15].

This assumption is known as the infinite alleles model in population genetics [29].

The additive hazard model is almost exclusively the first-choice model in population-based cancer studies.