Modeling over-dispersed datasets using generalized geometric distributions

Through this article we propose a class of over-dispersed distributions namely 'the generalized geometric distribution (GGD)' as an extension of the well-known 'geometric distribution'. Also, we develop an extended version of it and named it as 'the extended generalized geom...

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Bibliographic Details
Published in:Journal of statistical computation and simulation 2020-03, Vol.90 (4), p.606-623
Main Authors: Kumar, C. Satheesh, Harisankar, S.
Format: Article
Language:English
Subjects:
generalized likelihood ratio test
maximum likelihood estimation
model selection
Probability generating function
simulation
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Summary:Through this article we propose a class of over-dispersed distributions namely 'the generalized geometric distribution (GGD)' as an extension of the well-known 'geometric distribution'. Also, we develop an extended version of it and named it as 'the extended generalized geometric distribution (EGGD)'. Several properties of these classes of distributions are studied here. The maximum likelihood estimation of the parameters of the EGGD is discussed and fitted to certain real-life data sets for illustrating its usefulness compared to the existing models. Further, the generalized likelihood ratio test procedure is considered for testing the significance of the additional parameter of the EGGD model and a simulation study is carried out for assessing the performance of the estimators of each parameter of the model.
ISSN:0094-9655
1563-5163
DOI:10.1080/00949655.2019.1692841