Examples of using Negative binomial in English and their translations into Portuguese
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Ecclesiastic
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Ecclesiastic
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Computer
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A negative binomial regression models with fixed effects was used,
We used glms, negative binomial family, which combined with the information-theoretic approach led the choice of parsimonious models that associate the relationship between the.
severity of the accidents were analyzed using econometric models: negative binomial and ordered logit, respectively.
The results of negative binomial regression models with fixed effects for the outcomes related to infant,
Associations were estimated using negative binomial regression coefficients and confidence intervals at 95%,
In this sense, the Poisson and Negative Binomial regression models are alternative estimates.
The negative binomial regression adjusted model showed increased risk of death in more urbanized municipalities,
The difference between negative binomial regression and Poisson regression resides in its estimation of variance, which incorporates an over-dispersion parameter- alpha.
based on the negative binomial model by maximum likelihood.
Another variance function proposed is negative binomial probability distribution obtaining estimates of parameters by maximum likelihood method.
analyze the mortality trend in each state of Brazil, negative binomial regression models were adjusted. These belong to the class of generalized linear models.
so the regression model adopted is the negative binomial Long and Freese, 2001.
The associations have been estimated using negative binomial regression coefficients(β) and respective 95% confidence intervals, with a hierarchical approach in three levels alpha 5.
the geometric distribution, and the negative binomial distribution.
It should be noted that we performed the statistical procedures of the negative binomial regression technique using Stata software version 14.
Results of the negative binomial regression multiple model of the association between dental caries
The DMFT and MR means were obtained based on independent variables from the simple model of negative binomial regression Table 2.
of relative risk determination, obtained through a negative binomial distribution model.
This did not generate divergence between models, despite the negative binomial regression not considering auto-correlation in time.
the neural network was less sensitive than negative binomial regression.