negative binomial distribution

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negative binomial distribution

[¦neg·əd·iv bī¦nō·mē·əl ‚di·strə′byü·shən]
(statistics)
The distribution of a negative binomial random variable. Also known as Pascal distribution.
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We investigated the effect of various epidemiologic and meteorologic variables on scarlet fever incidence by using hierarchical multivariable negative binomial regression, accounting for autocorrelation and annual and biannual seasonal trends by using Fourier terms (i.
The negative binomial regression model is frequently used to model over-dispersed, non-negative count outcomes.
For Mixon, the negative binomial distribution with q = 2.
As the frequency of unhealthy snacks consumption (outcome variable) is a count variable, with an over-dispersion (without zero inflation), negative binomial regression model was used for data analysis.
19) Following Lawson's approach, (26) we identified the negative binomial distribution as the appropriate model function for the data.
To test the predictive value of baseline measures of hedonic response to pleasant images, self-report anhedonia, and future orientation on alcohol, marijuana, and NMPO use at follow-up, we conducted separate negative binomial regression models for alcohol and marijuana use (Atkins et al.
We then assessed the association of environmental triggers and built environment factors with inhaler use by implementing zero-truncated negative binomial models, and validating these results using three sensitivity analyses, which we describe in further detail.
We also considered negative binomial (Linden and Mantyniemi, 2011) and zero-inflated models (Agarwal et al.
For each class of models, we fitted 2 different distributions: the Poisson and the negative binomial (White and Bennetts, 1996).
x]) (GREEN, 1966), and k parameter of negative binomial distribution (BLISS and FISHER, 1953; SOUTHWOOD, 1978; ELLIOT, 1979), calculated using the maximum likelihood method.
The last step in the inferential statistical analysis was the multivariate regression analysis in which, for each outcome variable, a negative binomial regression model was estimated (Table 6).