The generalized gamma (GG) is a flexible distribution in statistical literature with the special cases of exponential, gamma, Weibull and lognormal distributions. This paper investigates the GG additive model for modeling hospital claim costs. In comparison to other models, the GG is more flexible and has a better performance in modeling positively skewed data. The proposed model was fitted to the hospital costs data from the nationwide inpatient sample of the health care cost and utilization project, a nationwide survey of hospital costs conducted by the U.S. Agency for healthcare research and quality. The results indicate that the claim cost is affected by the given explanatory variables and based on the AIC and BIC criteria, the GG has a better performance for the given data compared to the alternatives.
Zamani, H., Shekari, M., & Pakdaman, Z. (2020). Modeling insurance data using generalized gamma regression. Journal of Statistical Modelling: Theory and Applications, 1(1), 169-177. doi: 10.22034/jsmta.2020.1733
MLA
Hossein Zamani; Marzieh Shekari; Zohreh Pakdaman. "Modeling insurance data using generalized gamma regression", Journal of Statistical Modelling: Theory and Applications, 1, 1, 2020, 169-177. doi: 10.22034/jsmta.2020.1733
HARVARD
Zamani, H., Shekari, M., Pakdaman, Z. (2020). 'Modeling insurance data using generalized gamma regression', Journal of Statistical Modelling: Theory and Applications, 1(1), pp. 169-177. doi: 10.22034/jsmta.2020.1733
VANCOUVER
Zamani, H., Shekari, M., Pakdaman, Z. Modeling insurance data using generalized gamma regression. Journal of Statistical Modelling: Theory and Applications, 2020; 1(1): 169-177. doi: 10.22034/jsmta.2020.1733
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