Adaptive Regression Model for Highly Skewed Count Data.
| dc.contributor.author | Adesina, Olumide Sunday | |
| dc.date.accessioned | 2021-03-12T16:30:58Z | |
| dc.date.available | 2021-03-12T16:30:58Z | |
| dc.date.issued | 2019-01-11 | |
| dc.description.abstract | A big task often faced by practitioners is in deciding the appropriate model to adopt in fitting count datasets. This paper is aimed at investigating a suitable model for fitting highly skewed count datasets. Among other models, COM-Poisson regression model was proposed in this paper for fitting count data due to its varying normalizing constant. Some statistical models were investigated along with the proposed model; these include Poisson, Negative Binomial, Zero-Inflated, Zero-inflated Poisson and Quasi- Poisson models. A real life dataset relating to visits to Doctor within a given period was equally used to test the behavior of the underlying models. From the findings, it is recommended that COM-Poisson regression model should be adopted in fitting highly skewed count datasets irrespective of the type of dispersion. | en_US |
| dc.description.sponsorship | Covenant University | en_US |
| dc.identifier.uri | http://www.iaeme.com/ijmet/issues.asp?JType=IJMET&VType=10&IType=01 | |
| dc.identifier.uri | http://dspace.run.edu.ng:8080/jspui/handle/123456789/198 | |
| dc.language.iso | en | en_US |
| dc.publisher | IAEME | en_US |
| dc.title | Adaptive Regression Model for Highly Skewed Count Data. | en_US |
| dc.type | Article | en_US |