Statistical Inference Of Accelerated Failure Time Regression Model Based On Generalized Exponential Distribution

Survival analysis is a statistical method proposed for survival time.Accelerated failure time model is one of the most popular models in survival analysis.Used to research the covariate and logarithmic survival time between the regression relationship.The model is close to the general linear regression equation.The interpretation of the regression coefficient is similar to that of the general linear regression,and the accelerated failure time model has some advantages in fitting the survival data.The first work of this paper is to study the accelerated failure regression model based on Generalized exponential distribution.We are concerned about how the covariate affects the censored variable.According to the characteristics of the right censored data,the parameter regression model is established.The maximum likelihood estimation method and the Newton iterative algorithm are used to estimate the parameters and the regression coefficients.The simulation results show the validity of the model and apply it to women's breast cancer Disease data,indicating that the model has good applicability.The second work of this paper is to establish the accelerated failure regression model based on three-parameter Weibull distribution,and construct a maximum likelihood function for the type ? interval censored data.Because the position parameter satisfies t ?? in the three-parameter Weibull distribution,the constraint of the likelihood function is exp(log t-x??)-? ?0.The parameters are estimated by the method of maximum likelihood estimation.The simulation results show that the model is valid.Applying the model to hemophilia patients is data on potential populations of HIV infection,demonstrating their applicability.It is hoped that the results of this study will provide some help for the diagnosis of clinical diseases.