negative binomial distribution

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negative binomial distribution

A distribution parameterised by a mean and an aggregation parameter that is large when aggregation is small; as it becomes larger, the negative binomial distribution approximates a Poisson distribution. Aggregated distributions are often well described empirically by the negative binomial distribution. For instance, macroparasites are typically aggregated in their host populations, such that most hosts harbour few or no parasites while a few have large parasite burdens.
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4) of the negative binomial distribution yield the estimate that 8% of students account for 48% of self-reported cheating.
The range of probabilities of negative binomial distribution is obtained using the following calculation (JOHNSON and KOTZ, 1969):
26) when the negative binomial distribution was fitted to data from large Ebola transmission chains in Guinea (32); this result suggests that the high variability assumption may be appropriate, but whether or not the assumption of high variability is an appropriate characterization for potential Ebola outbreaks in new countries is unclear.
The dashed line represents the fitted negative binomial distribution while the columns correspond to the actual frequency density.
This finding was analogous to the work of Pollard et al (1977) when fitting the negative binomial distribution to groups of players.
In the next column, we report the maximum likelihood estimates of the two parameter negative binomial distribution.
This joint distribution can be called zero-inflated multivariate negative binomial (ZI-MVNB) because it has the form of a multivariate negative binomial distribution with a zero-inflated term equal to [[phi].
Residual variation was modeled by a negative binomial distribution (White & Bennetts 1996).
a] comes from a truncated negative binomial distribution for [y.
Some specific topics include properties of the three-parameter Burr XII distribution, empirical Bayes estimators for the reproduction parameter of Borel-Tanner distribution, estimation of the parameters of the inverse Weibull distribution with progressive censoring data, and application of the zero-truncated negative binomial distribution to the reliability theory and related Bayesian estimation procedures.
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