Sentence examples for binomial criteria from inspiring English sources

Evaluating the onset of this blepharoconjunctivitis as it relates to arthritis progression using basic, binomial criteria, we found that the milder form of blepharitis, which localized to the posterior eyelid, occurred spontaneously in approximately 25%% of SKG mice compared with 0%% in healthy BALB/c control mice (data not shown).

We calculated binomial standard errors.

The criteria were binomial SUM variables (smoking vs non-smoking; harmful-drinking vs non-harmful-drinking; multiple SUM vs non-multiple SUM).

From a non-asymptotic point of view and for the negative binomial model, the following criterion was proposed [ 25]: denoting m ^ K the optimal segmentation of the data in K segments, (2) K ^ = arg min K ∈ 1 : K max ∑ r ∈ m ^ K ∑ t ∈ r - ϕ log ϕ ϕ + y ¯ r - y t log 1 - ϕ ϕ + y ¯ r + βK 1 + 4 1.1 + log n K 2, where y ¯ r = ∑ t ∈ r y t n ^ r and n ^ r is the size of segment r.

To ensure that future water quality remains consistent with the reference period, new data for 3-year assessment periods could be evaluated against these criteria using a binomial test.

Using the type II negative binomial model (lowest Akaike information criterion and sigma coefficient = 8) and including the presence of roads and lakeside location, we found that the number of cholera cases in each health district in DRC was significantly higher in areas with roads (risk ratio [RR] 1.4, 95% confidence interval 1.1 1.9) and lakes (RR 7.0, 95% confidence interval 4.9 10.0).

Data for each criteria were processed as binomial distributions.

Using Akaike's information criterion (AIC), the negative binomial was a substantially better fit to our data than the Poisson (Poisson AIC = 15.58, negative binomial AIC = 4.45; Poisson is 0.0034 as likely to explain the data).

(The glm model with Poisson distribution was selected after comparing different models, including negative binomial regression, using the Akaike information criterion).

This was compared with the expected rate given by the proportional chance criterion using an exact binomial test (one-sided) to test the null hypothesis that the given success rate of classification was no better than chance.

For each contig, the probability of meeting the selection criteria was calculated using the binomial distribution, the percentage of all ESTs that were in salivary gland libraries (12.1%), and the number of ESTs that contributed to the given contig.