Examples of 'negative binomial' in a sentence
Meaning of "negative binomial"
negative binomial - In statistics, the negative binomial distribution is a discrete probability distribution that models the number of failures in a sequence of independent and identically distributed Bernoulli trials before a specified (non-random) number of successes occurs
How to use "negative binomial" in a sentence
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negative binomial
A negative binomial model has also been proposed.
Relationships are estimated with negative binomial regression models.
The negative binomial regression model.
A geometric distribution is a special case of negative binomial distribution.
Negative binomial regression models were calculated.
A model using negative binomial regression.
A negative binomial regression model was employed to analyze the data.
The charged multiplicities are shown to have a negative binomial distribution.
A negative binomial model was used to study the trends.
These distributions can be modeled by the negative binomial distribution.
The negative binomial distribution may also be helpful.
Compute mean and variance of the negative binomial distribution.
Beta negative binomial distribution.
The statistical analyses were conducted using a negative binomial regression approach.
Extended negative binomial distribution.
See also
Both these tools use a model based on the negative binomial distribution.
Bivariate negative binomial distribution.
The research method applied in this paper is the negative binomial regression.
Multivariate negative binomial distribution.
Negative binomial regression.
Econometric estimation of the travel cost model is performed here with negative binomial model.
Decapitated negative binomial distribution.
Compounding a Poisson distribution with rate parameter distributed according to a gamma distribution yields a negative binomial distribution.
Thus negative binomial regression turns out to be the most appropriate.
The dependent variable is assumed to have a Negative binomial error distribution.
The negative binomial variables may be interpreted as random waiting times.
It can be defined using the convolution of a negative binomial distribution with a Poisson distribution.
Negative binomial regression assessed the trend in hospitalisation rates over time.
If the scaling holds we can generalize the negative binomial distribution in accordance with QCD.
Negative binomial regression was used due to the presence in over dispersed in the data.
As a result both the Poisson model and the Negative binomial model did not converge.
The negative binomial and logistic regressions were performed with all covariates measured on baseline.
Which is a distinguishing feature of the negative Binomial Distribution.
The negative binomial is favored over the Poisson.
Table 3 shows results from negative binomial regression.
A negative binomial distribution with n 1 is a geometric distribution.
For continuous outcomes, negative binomial regressions were conducted.
Negative binomial models were developed using 5 year collision data.
An example of super-Poissonian distribution is negative binomial distribution.
The negative binomial distribution is characterized by the following function,.
The negative multinomial distribution, a generalization of the negative binomial distribution.
For this reason, negative binomial regression models are estimated in those cases.
Methods for count data, including poisson and negative binomial models.
The negative binomial regression does not integrate the length of inter-fall intervals.
These will not be geometrically distributed unless n 1 ; they follow a negative binomial distribution.
As a result, the negative binomial model is preferred to the Poisson model.
First, an illustrative example is presented and analyzed under parametric negative binomial regression models.
In this way, the negative binomial distribution is seen to be a compound Poisson distribution.
Finally, we discuss about modeling sales of used cars by using the negative binomial distribution.
Note, The negative binomial distribution was originally derived as a limiting case of the gamma-Poisson distribution.
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