廣義泊松回歸模型的推廣及其在醫(yī)療保險中應用
發(fā)布時間：2018-07-31 05:30
【摘要】：計數數據往往存在過離散(over-dispersed)即方差大于均值特征,若利用傳統(tǒng)的泊松回歸模型擬合數據往往會導致其參數的標準誤差被低估,顯著性水平被高估的錯誤結論。負二項回歸模型、廣義泊松回歸模型通常被用來處理過離散特征數據。本文以兩類廣義泊松回歸模型GP-1和GP-2模型為基礎,將其推廣為更為一般的GP-P形式,其中P為參數。此時,P=1或P=2,GP-P模型就退化為GP-1和GP-2模型。文中最后利用此類推廣的GP-P模型處理了一組醫(yī)療保險數據,并與泊松回歸模型、負二項回歸模型擬合結果進行了比較。結果表明,推廣后的GP-P模型的擬合效果更優(yōu)。
[Abstract]:The over-dispersed of the counting data is always larger than the mean value. If the traditional Poisson regression model is used to fit the data, the standard error of its parameters will be underestimated, and the significance level will be overestimated. Negative binomial regression model and generalized Poisson regression model are usually used to deal with discrete characteristic data. Based on two kinds of generalized Poisson regression models, GP-1 and GP-2, this paper generalizes them to a more general GP-P form, where P is a parameter. At this time, the GP-P model of PX 1 or PJ 2H degenerated into GP-1 and GP-2 models. Finally, a group of medical insurance data is processed by using this extended GP-P model, and the fitting results are compared with Poisson regression model and negative binomial regression model. The results show that the fitting effect of the extended GP-P model is better.
【作者單位】： 首都經濟貿易大學金融學院;
【基金】：2015年中國保監(jiān)會部級課題項目支持 國家社會科學基金項目(12CJY114)支持
【分類號】：O212.1
本文編號：2154574
[Abstract]:The over-dispersed of the counting data is always larger than the mean value. If the traditional Poisson regression model is used to fit the data, the standard error of its parameters will be underestimated, and the significance level will be overestimated. Negative binomial regression model and generalized Poisson regression model are usually used to deal with discrete characteristic data. Based on two kinds of generalized Poisson regression models, GP-1 and GP-2, this paper generalizes them to a more general GP-P form, where P is a parameter. At this time, the GP-P model of PX 1 or PJ 2H degenerated into GP-1 and GP-2 models. Finally, a group of medical insurance data is processed by using this extended GP-P model, and the fitting results are compared with Poisson regression model and negative binomial regression model. The results show that the fitting effect of the extended GP-P model is better.
【作者單位】： 首都經濟貿易大學金融學院;
【基金】：2015年中國保監(jiān)會部級課題項目支持 國家社會科學基金項目(12CJY114)支持
【分類號】：O212.1
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