Significats de poisson model en anglès

A zero-inflated Poisson model clustered by school tested the prediction of school-level variables.

Methods: Quasi- Poisson models were fitted separately to weekly numbers of deaths from various causes.

The final analysis used autoregressive Poisson models allowing for overdispersion.

Zero-inflated Poisson models were used to assess differences in absenteeism between cyclists and non-cyclists.

Standardized incidence ratios were calculated by a Poisson model.

Group-based trajectory analyses were conducted with zero-inflated Poisson models.

Most of existing sample size calculation methods for count outcomes are developed under the Poisson model.

The assumption of a Gaussian noise model yielded only slightly higher errors than the Poisson model.

Poisson models with natural splines were used to control for time-varying covariates such as season and weather.

We used a two-parameter hurdle Poisson model to simultaneously analyse the zero counts and the frequency of occurrence.

The effects of the new DWTP and fecal contamination levels on infections were tested using logistic and Poisson models.

A modified Poisson model was used to identify factors associated with failure to initiate ART rapidly under treat all.

We used Poisson models to obtain rate ratios (RRs) for incident IPD associated with HIV infection and other risk factors.

Two of these (the GS and PS models) are better than the Poisson model for the clinically relevant cases tested here.

The identification was conducted using a retrospective space-time analysis scan for statistically significant clusters with high or low rates by a Discrete Poisson Model.

The result of the statistical slope test can then be graphed to visualize whether the data are compatible or not with the single-hit Poisson model.