Poisson Extended Exponential Distribution with Associated INAR(1) Process and Applications

The significance of count data modeling and its applications to real-world phenomena have been highlighted in several research studies. The present study focuses on a two-parameter discrete distribution that can be obtained by compounding the Poisson and extended exponential distributions. It has tr...

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Bibliographic Details
Published in:Stats (Basel, Switzerland) Switzerland), 2022-08, Vol.5 (3), p.755-772
Main Authors: Maya, Radhakumari, Chesneau, Christophe, Krishna, Anuresha, Irshad, Muhammed Rasheed
Format: Article
Language:English
Subjects:
Bias
Computer simulation
Datasets
extended exponential distribution
INAR process
Innovations
Maximum likelihood estimates (Statistics)
Maximum likelihood method
Monte Carlo simulation
Optimization techniques
overdispersion
Parameter estimation
Poisson distribution
Probability
Random variables
regression model
simulation
Time series
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Summary:The significance of count data modeling and its applications to real-world phenomena have been highlighted in several research studies. The present study focuses on a two-parameter discrete distribution that can be obtained by compounding the Poisson and extended exponential distributions. It has tractable and explicit forms for its statistical properties. The maximum likelihood estimation method is used to estimate the unknown parameters. An extensive simulation study was also performed. In this paper, the significance of the proposed distribution is demonstrated in a count regression model and in a first-order integer-valued autoregressive process, referred to as the INAR(1) process. In addition to this, the empirical importance of the proposed model is proved through three real-data applications, and the empirical findings indicate that the proposed INAR(1) model provides better results than other competitive models for time series of counts that display overdispersion.
ISSN:2571-905X
2571-905X
DOI:10.3390/stats5030044