On the Generalized Poisson Regression Model with an Application to Accident Data

In this paper a random sample of drivers aged sixty-five years or older was selected from the Alabama Department of Public Safety Records. The data in the sample has information on many variables including the number of accidents, demographic information, driving habits, and medication. The purpose of the sample was to assess the effects of demographic factors, driving habits, and medication use on elderly drivers. The generalized Poisson regression (GPR) model is considered for identifying the relationship between the number of accidents and some covariates. About 59% of drivers who rate their quality of driving as average or below are involved in automobile accidents. Drivers who take calcium channel blockers show a significantly reduced risk of about 34.5%. Based on the test for the dispersion parameter and the goodness-of-fit measure for the accident data, the GPR model performs as good as or better than the other regression models.

並列關鍵字

Dispersion ； elderly drivers ； goodness-of-fit measure ； maximum likelihood estimation

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On the Generalized Poisson Regression Model with an Application to Accident Data

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