Sentence examples for mean count models from inspiring English sources

Todem et al. (2016) described a general representation of two-part marginalized mean count models including distributions for bounded counts, e.g., the zero-inflated beta-binomial distribution.

In terms of PSs, the mean count was 4.06 in the heterogeneous model (maximum 14), compared with 4.41 in the homogeneous model (maximum 12).

The sub-canopy count models resulted in mean differences of 0.1 1.4 trees, RMSE of 3.0 13.4 trees, and 95% confidence interval widths of 1.1 5.0 trees, depending on species.

Regression parameters were specified in two-part marginalized models for direct estimation of exposure effects on the overall mean count using maximum likelihood methods.

Their mean count, or piecewise constant event rate, can be evaluated by discrete probability distributions from the Poisson model family.

The mean count so generated is 1.9.

Because our main interest is in modeling marginal means of counts, model parameters that are not of primary interest are allowed to depend on covariates, or none whatsoever, to complete specification of the likelihood function.

While a Poisson distribution may be assumed in count data on the number of visits to a provider, the actual estimation of the count model by means of Poisson estimation methods may be inappropriate due to the rather restrictive assumptions of the Poisson model.

Having an assumption of the equality of the distributional mean and variance, Poisson models are inappropriate for many count-modeling situations.

Irak2 foldchanges calculated from normalised mean counts show overexpression of exon-bins 7 17, 20 and 21 in the Irak2 gene model which standard DexSeq analysis failed to detect.

Absolute counts were higher inside than outside clearcuts for galleries (mean counts; inside: 1.8, outside: 0.8).