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Hussain and Ahmad (2014) applied this distribution to model two real life count data.
This distribution was applied to model two real life count data.
On the other hand, we choose in the model two real numbers α and γ, so that ∫ 0 h k 1 ( τ 1 ) d τ 1 = 1 and ∫ 0 h k 2 ( τ 2 ) d τ 2 = 1.
The distribution is applied to model two real life data sets; one consisting of the observed frequencies of the distribution of purchases of a brand X breakfast cereals, and the other, the observed frequencies of the number of absences among shift-workers in a steel industry.
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To validate the trained model, four real recorded earthquake signals are used.
In this section, the PCNB distribution is fitted to model three real life data sets.
Chakraborty and Chakravarty (2014) applied this distribution to model three real life count data related to maximum flood discharges and annual maximum wind speeds from literature.
Application of the distribution in modelling two real life count data sets was also demonstrated by the author.
In this section we compare our current CPBA model with our original Pure Propensity Poisson (PPP) model on two real datasets: the OMIM disease network and the complimentary OMIM gene network.
Then, we apply our model to two real data sets with implicit temporal or spatial regulation variations.
We then apply the model to two real examples from the literature, one involving a diagnostic marker and one involving a surrogate outcome, and these both give a between-study correlation estimate of -1.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com