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Statistical Models for Count Data

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dc.contributor.author Kasyoki Muoka, Alexander
dc.contributor.author Owino Ngesa, Oscar
dc.contributor.author Gichuhi Waititu, Anthony
dc.date.accessioned 2021-06-16T06:42:00Z
dc.date.available 2021-06-16T06:42:00Z
dc.date.issued 2016
dc.identifier.issn 2376-9513
dc.identifier.uri http://ir.ttu.ac.ke/xmlui/handle/123456789/45
dc.description.abstract Statistical analyses involving count data may take several forms depending on the context of use, that is; simple counts such as the number of plants in a particular field and categorical data in which counts represent the number of items falling in each of the several categories. The mostly adapted model for analyzing count data is the Poisson model. Other models that can be considered for modeling count data are the negative binomial and the hurdle models. It is of great importance that these models are systematically considered and compared before choosing one at the expense of others to handle count data. In real world situations count data sets may have zero counts which have an importance attached to them. In this work, statistical simulation technique was used to compare the performance of these count data models. Count data sets with different proportions of zero were simulated. Akaike Information Criterion (AIC) was used in the simulation study to compare how well several count data models fit the simulated datasets. From the results of the study it was concluded that negative binomial model fits better to over-dispersed data which has below 0.3 proportion of zeros and that hurdle model performs better in data with 0.3 and above proportion of zero. en_US
dc.language.iso en en_US
dc.publisher Science Journal of Applied Mathematics and Statistics en_US
dc.subject Count, Modeling, Simulation, AIC, Compare en_US
dc.title Statistical Models for Count Data en_US
dc.type Article en_US

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