Sentence examples for binomial models were estimated from inspiring English sources

The marginal effects of dummy variables in both generalized linear model and negative binomial models were estimated using the method of recycled predictions (22).

County-level time-series data is used and fixed effect negative binomial models are estimated.

Negative-binomial regression models were estimated using generalized estimating equations assuming an autoregressive covariance structure.

The models were estimated with a negative binomial distribution using Maximum Likelihood estimators.

The GEE models were estimated using a binomial distribution, independent working correlation matrix and a logit link function.

The univariable models were estimated using the binomial logistic regression.

In this study two regression models were estimated: Poisson and logit binomial.

In addition, the bivariate negative binomial (BNB) and two individual univariate ZINB models are estimated for model validation.

Specifically, a negative binomial count model was estimated due to overdispersion of total injuries.

Separate standard negative binomial models were used to estimate incidence rate ratios (IRRs) and 95% confidence intervals (CIs) for the frequency of each ACM outcome (counts/10,000 sperm) among low-, moderand-, and high-exposed men compared with unexposed men.

Negative binomial models were used to estimate associations between the monthly number of hospital visits for cholera in Dhaka and Matlab (1993 2007) and the dipole mode index (DMI) controlling for ENSO index [NINO3, a measure of the average sea surface temperature (SST) in the Niño 3 region], seasonal, and interannual variations.