Using the Discrete Lindley Distribution to Deal with Over-dispersion in Count Data
Issued Date
2023-07-18
Resource Type
ISSN
1026597X
Scopus ID
2-s2.0-85165905084
Journal Title
Austrian Journal of Statistics
Volume
52
Issue
3
Start Page
96
End Page
113
Rights Holder(s)
SCOPUS
Bibliographic Citation
Austrian Journal of Statistics Vol.52 No.3 (2023) , 96-113
Suggested Citation
Nguyen M.T.N., Nguyen M.V.M., Le N.T. Using the Discrete Lindley Distribution to Deal with Over-dispersion in Count Data. Austrian Journal of Statistics Vol.52 No.3 (2023) , 96-113. 113. doi:10.17713/ajs.v52i3.1465 Retrieved from: https://repository.li.mahidol.ac.th/handle/20.500.14594/88219
Title
Using the Discrete Lindley Distribution to Deal with Over-dispersion in Count Data
Author(s)
Author's Affiliation
Other Contributor(s)
Abstract
Count data in environmental epidemiology or ecology often display substantial over-dispersion, and failing to account for the over-dispersion could result in biased estimates and underestimated standard errors. This study develops a new generalized linear model family to model over-dispersed count data by assuming that the response variable follows the discrete Lindley distribution. The iterative weighted least square is developed to fit the model. Furthermore, asymptotic properties of estimators, the goodness of fit statistics are also derived. Lastly, some simulation studies and empirical data applications are carried out, and the generalized discrete Lindley linear model shows a better performance than the Poisson distribution model.